By “group by” we are referring to a process involving one or more of the following steps:
Splitting the data into groups based on some criteria.
Applying a function to each group independently.
Combining the results into a data structure.
Out of these, the split step is the most straightforward. In fact, in many situations we may wish to split the data set into groups and do something with those groups. In the apply step, we might wish to do one of the following:
Aggregation: compute a summary statistic (or statistics) for each group. Some examples:
Compute group sums or means. Compute group sizes / counts.
Compute group sums or means.
Compute group sizes / counts.
Transformation: perform some group-specific computations and return a like-indexed object. Some examples:
Standardize data (zscore) within a group. Filling NAs within groups with a value derived from each group.
Standardize data (zscore) within a group.
Filling NAs within groups with a value derived from each group.
Filtration: discard some groups, according to a group-wise computation that evaluates True or False. Some examples:
Discard data that belongs to groups with only a few members. Filter out data based on the group sum or mean.
Discard data that belongs to groups with only a few members.
Filter out data based on the group sum or mean.
Some combination of the above: GroupBy will examine the results of the apply step and try to return a sensibly combined result if it doesn’t fit into either of the above two categories.
Since the set of object instance methods on pandas data structures are generally rich and expressive, we often simply want to invoke, say, a DataFrame function on each group. The name GroupBy should be quite familiar to those who have used a SQL-based tool (or itertools), in which you can write code like:
itertools
SELECT Column1, Column2, mean(Column3), sum(Column4) FROM SomeTable GROUP BY Column1, Column2
We aim to make operations like this natural and easy to express using pandas. We’ll address each area of GroupBy functionality then provide some non-trivial examples / use cases.
See the cookbook for some advanced strategies.
pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names. To create a GroupBy object (more on what the GroupBy object is later), you may do the following:
In [1]: df = pd.DataFrame([('bird', 'Falconiformes', 389.0), ...: ('bird', 'Psittaciformes', 24.0), ...: ('mammal', 'Carnivora', 80.2), ...: ('mammal', 'Primates', np.nan), ...: ('mammal', 'Carnivora', 58)], ...: index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'], ...: columns=('class', 'order', 'max_speed')) ...: In [2]: df Out[2]: class order max_speed falcon bird Falconiformes 389.0 parrot bird Psittaciformes 24.0 lion mammal Carnivora 80.2 monkey mammal Primates NaN leopard mammal Carnivora 58.0 # default is axis=0 In [3]: grouped = df.groupby('class') In [4]: grouped = df.groupby('order', axis='columns') In [5]: grouped = df.groupby(['class', 'order'])
The mapping can be specified many different ways:
A Python function, to be called on each of the axis labels.
A list or NumPy array of the same length as the selected axis.
A dict or Series, providing a label -> group name mapping.
Series
label -> group name
For DataFrame objects, a string indicating a column to be used to group. Of course df.groupby('A') is just syntactic sugar for df.groupby(df['A']), but it makes life simpler.
DataFrame
df.groupby('A')
df.groupby(df['A'])
For DataFrame objects, a string indicating an index level to be used to group.
A list of any of the above things.
Collectively we refer to the grouping objects as the keys. For example, consider the following DataFrame:
Note
A string passed to groupby may refer to either a column or an index level. If a string matches both a column name and an index level name, a ValueError will be raised.
groupby
ValueError
In [6]: df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', ...: 'foo', 'bar', 'foo', 'foo'], ...: 'B': ['one', 'one', 'two', 'three', ...: 'two', 'two', 'one', 'three'], ...: 'C': np.random.randn(8), ...: 'D': np.random.randn(8)}) ...: In [7]: df Out[7]: A B C D 0 foo one 0.469112 -0.861849 1 bar one -0.282863 -2.104569 2 foo two -1.509059 -0.494929 3 bar three -1.135632 1.071804 4 foo two 1.212112 0.721555 5 bar two -0.173215 -0.706771 6 foo one 0.119209 -1.039575 7 foo three -1.044236 0.271860
On a DataFrame, we obtain a GroupBy object by calling groupby(). We could naturally group by either the A or B columns, or both:
groupby()
A
B
In [8]: grouped = df.groupby('A') In [9]: grouped = df.groupby(['A', 'B'])
New in version 0.24.
If we also have a MultiIndex on columns A and B, we can group by all but the specified columns
In [10]: df2 = df.set_index(['A', 'B']) In [11]: grouped = df2.groupby(level=df2.index.names.difference(['B'])) In [12]: grouped.sum() Out[12]: C D A bar -1.591710 -1.739537 foo -0.752861 -1.402938
These will split the DataFrame on its index (rows). We could also split by the columns:
In [13]: def get_letter_type(letter): ....: if letter.lower() in 'aeiou': ....: return 'vowel' ....: else: ....: return 'consonant' ....: In [14]: grouped = df.groupby(get_letter_type, axis=1)
pandas Index objects support duplicate values. If a non-unique index is used as the group key in a groupby operation, all values for the same index value will be considered to be in one group and thus the output of aggregation functions will only contain unique index values:
Index
In [15]: lst = [1, 2, 3, 1, 2, 3] In [16]: s = pd.Series([1, 2, 3, 10, 20, 30], lst) In [17]: grouped = s.groupby(level=0) In [18]: grouped.first() Out[18]: 1 1 2 2 3 3 dtype: int64 In [19]: grouped.last() Out[19]: 1 10 2 20 3 30 dtype: int64 In [20]: grouped.sum() Out[20]: 1 11 2 22 3 33 dtype: int64
Note that no splitting occurs until it’s needed. Creating the GroupBy object only verifies that you’ve passed a valid mapping.
Many kinds of complicated data manipulations can be expressed in terms of GroupBy operations (though can’t be guaranteed to be the most efficient). You can get quite creative with the label mapping functions.
By default the group keys are sorted during the groupby operation. You may however pass sort=False for potential speedups:
sort=False
In [21]: df2 = pd.DataFrame({'X': ['B', 'B', 'A', 'A'], 'Y': [1, 2, 3, 4]}) In [22]: df2.groupby(['X']).sum() Out[22]: Y X A 7 B 3 In [23]: df2.groupby(['X'], sort=False).sum() Out[23]: Y X B 3 A 7
Note that groupby will preserve the order in which observations are sorted within each group. For example, the groups created by groupby() below are in the order they appeared in the original DataFrame:
In [24]: df3 = pd.DataFrame({'X': ['A', 'B', 'A', 'B'], 'Y': [1, 4, 3, 2]}) In [25]: df3.groupby(['X']).get_group('A') Out[25]: X Y 0 A 1 2 A 3 In [26]: df3.groupby(['X']).get_group('B') Out[26]: X Y 1 B 4 3 B 2
The groups attribute is a dict whose keys are the computed unique groups and corresponding values being the axis labels belonging to each group. In the above example we have:
groups
In [27]: df.groupby('A').groups Out[27]: {'bar': Int64Index([1, 3, 5], dtype='int64'), 'foo': Int64Index([0, 2, 4, 6, 7], dtype='int64')} In [28]: df.groupby(get_letter_type, axis=1).groups Out[28]: {'consonant': Index(['B', 'C', 'D'], dtype='object'), 'vowel': Index(['A'], dtype='object')}
Calling the standard Python len function on the GroupBy object just returns the length of the groups dict, so it is largely just a convenience:
len
In [29]: grouped = df.groupby(['A', 'B']) In [30]: grouped.groups Out[30]: {('bar', 'one'): Int64Index([1], dtype='int64'), ('bar', 'three'): Int64Index([3], dtype='int64'), ('bar', 'two'): Int64Index([5], dtype='int64'), ('foo', 'one'): Int64Index([0, 6], dtype='int64'), ('foo', 'three'): Int64Index([7], dtype='int64'), ('foo', 'two'): Int64Index([2, 4], dtype='int64')} In [31]: len(grouped) Out[31]: 6
GroupBy will tab complete column names (and other attributes):
GroupBy
In [32]: df Out[32]: height weight gender 2000-01-01 42.849980 157.500553 male 2000-01-02 49.607315 177.340407 male 2000-01-03 56.293531 171.524640 male 2000-01-04 48.421077 144.251986 female 2000-01-05 46.556882 152.526206 male 2000-01-06 68.448851 168.272968 female 2000-01-07 70.757698 136.431469 male 2000-01-08 58.909500 176.499753 female 2000-01-09 76.435631 174.094104 female 2000-01-10 45.306120 177.540920 male In [33]: gb = df.groupby('gender')
In [34]: gb.<TAB> # noqa: E225, E999 gb.agg gb.boxplot gb.cummin gb.describe gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist gb.max gb.min gb.nth gb.prod gb.resample gb.sum gb.var gb.apply gb.cummax gb.cumsum gb.fillna gb.gender gb.head gb.indices gb.mean gb.name gb.ohlc gb.quantile gb.size gb.tail gb.weight
With hierarchically-indexed data, it’s quite natural to group by one of the levels of the hierarchy.
Let’s create a Series with a two-level MultiIndex.
MultiIndex
In [35]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] ....: In [36]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second']) In [37]: s = pd.Series(np.random.randn(8), index=index) In [38]: s Out[38]: first second bar one -0.919854 two -0.042379 baz one 1.247642 two -0.009920 foo one 0.290213 two 0.495767 qux one 0.362949 two 1.548106 dtype: float64
We can then group by one of the levels in s.
s
In [39]: grouped = s.groupby(level=0) In [40]: grouped.sum() Out[40]: first bar -0.962232 baz 1.237723 foo 0.785980 qux 1.911055 dtype: float64
If the MultiIndex has names specified, these can be passed instead of the level number:
In [41]: s.groupby(level='second').sum() Out[41]: second one 0.980950 two 1.991575 dtype: float64
The aggregation functions such as sum will take the level parameter directly. Additionally, the resulting index will be named according to the chosen level:
sum
In [42]: s.sum(level='second') Out[42]: second one 0.980950 two 1.991575 dtype: float64
Grouping with multiple levels is supported.
In [43]: s Out[43]: first second third bar doo one -1.131345 two -0.089329 baz bee one 0.337863 two -0.945867 foo bop one -0.932132 two 1.956030 qux bop one 0.017587 two -0.016692 dtype: float64 In [44]: s.groupby(level=['first', 'second']).sum() Out[44]: first second bar doo -1.220674 baz bee -0.608004 foo bop 1.023898 qux bop 0.000895 dtype: float64
Index level names may be supplied as keys.
In [45]: s.groupby(['first', 'second']).sum() Out[45]: first second bar doo -1.220674 baz bee -0.608004 foo bop 1.023898 qux bop 0.000895 dtype: float64
More on the sum function and aggregation later.
A DataFrame may be grouped by a combination of columns and index levels by specifying the column names as strings and the index levels as pd.Grouper objects.
pd.Grouper
In [46]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']] ....: In [47]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second']) In [48]: df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3], ....: 'B': np.arange(8)}, ....: index=index) ....: In [49]: df Out[49]: A B first second bar one 1 0 two 1 1 baz one 1 2 two 1 3 foo one 2 4 two 2 5 qux one 3 6 two 3 7
The following example groups df by the second index level and the A column.
df
second
In [50]: df.groupby([pd.Grouper(level=1), 'A']).sum() Out[50]: B second A one 1 2 2 4 3 6 two 1 4 2 5 3 7
Index levels may also be specified by name.
In [51]: df.groupby([pd.Grouper(level='second'), 'A']).sum() Out[51]: B second A one 1 2 2 4 3 6 two 1 4 2 5 3 7
Index level names may be specified as keys directly to groupby.
In [52]: df.groupby(['second', 'A']).sum() Out[52]: B second A one 1 2 2 4 3 6 two 1 4 2 5 3 7
Once you have created the GroupBy object from a DataFrame, you might want to do something different for each of the columns. Thus, using [] similar to getting a column from a DataFrame, you can do:
[]
In [53]: grouped = df.groupby(['A']) In [54]: grouped_C = grouped['C'] In [55]: grouped_D = grouped['D']
This is mainly syntactic sugar for the alternative and much more verbose:
In [56]: df['C'].groupby(df['A']) Out[56]: <pandas.core.groupby.generic.SeriesGroupBy object at 0x7f2d4d666910>
Additionally this method avoids recomputing the internal grouping information derived from the passed key.
With the GroupBy object in hand, iterating through the grouped data is very natural and functions similarly to itertools.groupby():
itertools.groupby()
In [57]: grouped = df.groupby('A') In [58]: for name, group in grouped: ....: print(name) ....: print(group) ....: bar A B C D 1 bar one 0.254161 1.511763 3 bar three 0.215897 -0.990582 5 bar two -0.077118 1.211526 foo A B C D 0 foo one -0.575247 1.346061 2 foo two -1.143704 1.627081 4 foo two 1.193555 -0.441652 6 foo one -0.408530 0.268520 7 foo three -0.862495 0.024580
In the case of grouping by multiple keys, the group name will be a tuple:
In [59]: for name, group in df.groupby(['A', 'B']): ....: print(name) ....: print(group) ....: ('bar', 'one') A B C D 1 bar one 0.254161 1.511763 ('bar', 'three') A B C D 3 bar three 0.215897 -0.990582 ('bar', 'two') A B C D 5 bar two -0.077118 1.211526 ('foo', 'one') A B C D 0 foo one -0.575247 1.346061 6 foo one -0.408530 0.268520 ('foo', 'three') A B C D 7 foo three -0.862495 0.02458 ('foo', 'two') A B C D 2 foo two -1.143704 1.627081 4 foo two 1.193555 -0.441652
See Iterating through groups.
A single group can be selected using get_group():
get_group()
In [60]: grouped.get_group('bar') Out[60]: A B C D 1 bar one 0.254161 1.511763 3 bar three 0.215897 -0.990582 5 bar two -0.077118 1.211526
Or for an object grouped on multiple columns:
In [61]: df.groupby(['A', 'B']).get_group(('bar', 'one')) Out[61]: A B C D 1 bar one 0.254161 1.511763
Once the GroupBy object has been created, several methods are available to perform a computation on the grouped data. These operations are similar to the aggregating API, window functions API, and resample API.
An obvious one is aggregation via the aggregate() or equivalently agg() method:
aggregate()
agg()
In [62]: grouped = df.groupby('A') In [63]: grouped.aggregate(np.sum) Out[63]: C D A bar 0.392940 1.732707 foo -1.796421 2.824590 In [64]: grouped = df.groupby(['A', 'B']) In [65]: grouped.aggregate(np.sum) Out[65]: C D A B bar one 0.254161 1.511763 three 0.215897 -0.990582 two -0.077118 1.211526 foo one -0.983776 1.614581 three -0.862495 0.024580 two 0.049851 1.185429
As you can see, the result of the aggregation will have the group names as the new index along the grouped axis. In the case of multiple keys, the result is a MultiIndex by default, though this can be changed by using the as_index option:
as_index
In [66]: grouped = df.groupby(['A', 'B'], as_index=False) In [67]: grouped.aggregate(np.sum) Out[67]: A B C D 0 bar one 0.254161 1.511763 1 bar three 0.215897 -0.990582 2 bar two -0.077118 1.211526 3 foo one -0.983776 1.614581 4 foo three -0.862495 0.024580 5 foo two 0.049851 1.185429 In [68]: df.groupby('A', as_index=False).sum() Out[68]: A C D 0 bar 0.392940 1.732707 1 foo -1.796421 2.824590
Note that you could use the reset_index DataFrame function to achieve the same result as the column names are stored in the resulting MultiIndex:
reset_index
In [69]: df.groupby(['A', 'B']).sum().reset_index() Out[69]: A B C D 0 bar one 0.254161 1.511763 1 bar three 0.215897 -0.990582 2 bar two -0.077118 1.211526 3 foo one -0.983776 1.614581 4 foo three -0.862495 0.024580 5 foo two 0.049851 1.185429
Another simple aggregation example is to compute the size of each group. This is included in GroupBy as the size method. It returns a Series whose index are the group names and whose values are the sizes of each group.
size
In [70]: grouped.size() Out[70]: A B bar one 1 three 1 two 1 foo one 2 three 1 two 2 dtype: int64
In [71]: grouped.describe() Out[71]: C ... D count mean std min 25% ... min 25% 50% 75% max 0 1.0 0.254161 NaN 0.254161 0.254161 ... 1.511763 1.511763 1.511763 1.511763 1.511763 1 1.0 0.215897 NaN 0.215897 0.215897 ... -0.990582 -0.990582 -0.990582 -0.990582 -0.990582 2 1.0 -0.077118 NaN -0.077118 -0.077118 ... 1.211526 1.211526 1.211526 1.211526 1.211526 3 2.0 -0.491888 0.117887 -0.575247 -0.533567 ... 0.268520 0.537905 0.807291 1.076676 1.346061 4 1.0 -0.862495 NaN -0.862495 -0.862495 ... 0.024580 0.024580 0.024580 0.024580 0.024580 5 2.0 0.024925 1.652692 -1.143704 -0.559389 ... -0.441652 0.075531 0.592714 1.109898 1.627081 [6 rows x 16 columns]
Aggregation functions will not return the groups that you are aggregating over if they are named columns, when as_index=True, the default. The grouped columns will be the indices of the returned object.
as_index=True
Passing as_index=False will return the groups that you are aggregating over, if they are named columns.
as_index=False
Aggregating functions are the ones that reduce the dimension of the returned objects. Some common aggregating functions are tabulated below:
Function
Description
mean()
Compute mean of groups
sum()
Compute sum of group values
size()
Compute group sizes
count()
Compute count of group
std()
Standard deviation of groups
var()
Compute variance of groups
sem()
Standard error of the mean of groups
describe()
Generates descriptive statistics
first()
Compute first of group values
last()
Compute last of group values
nth()
Take nth value, or a subset if n is a list
min()
Compute min of group values
max()
Compute max of group values
The aggregating functions above will exclude NA values. Any function which reduces a Series to a scalar value is an aggregation function and will work, a trivial example is df.groupby('A').agg(lambda ser: 1). Note that nth() can act as a reducer or a filter, see here.
df.groupby('A').agg(lambda ser: 1)
With grouped Series you can also pass a list or dict of functions to do aggregation with, outputting a DataFrame:
In [72]: grouped = df.groupby('A') In [73]: grouped['C'].agg([np.sum, np.mean, np.std]) Out[73]: sum mean std A bar 0.392940 0.130980 0.181231 foo -1.796421 -0.359284 0.912265
On a grouped DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:
In [74]: grouped.agg([np.sum, np.mean, np.std]) Out[74]: C D sum mean std sum mean std A bar 0.392940 0.130980 0.181231 1.732707 0.577569 1.366330 foo -1.796421 -0.359284 0.912265 2.824590 0.564918 0.884785
The resulting aggregations are named for the functions themselves. If you need to rename, then you can add in a chained operation for a Series like this:
In [75]: (grouped['C'].agg([np.sum, np.mean, np.std]) ....: .rename(columns={'sum': 'foo', ....: 'mean': 'bar', ....: 'std': 'baz'})) ....: Out[75]: foo bar baz A bar 0.392940 0.130980 0.181231 foo -1.796421 -0.359284 0.912265
For a grouped DataFrame, you can rename in a similar manner:
In [76]: (grouped.agg([np.sum, np.mean, np.std]) ....: .rename(columns={'sum': 'foo', ....: 'mean': 'bar', ....: 'std': 'baz'})) ....: Out[76]: C D foo bar baz foo bar baz A bar 0.392940 0.130980 0.181231 1.732707 0.577569 1.366330 foo -1.796421 -0.359284 0.912265 2.824590 0.564918 0.884785
In general, the output column names should be unique. You can’t apply the same function (or two functions with the same name) to the same column.
In [77]: grouped['C'].agg(['sum', 'sum']) Out[77]: sum sum A bar 0.392940 0.392940 foo -1.796421 -1.796421
Pandas does allow you to provide multiple lambdas. In this case, pandas will mangle the name of the (nameless) lambda functions, appending _<i> to each subsequent lambda.
_<i>
In [78]: grouped['C'].agg([lambda x: x.max() - x.min(), ....: lambda x: x.median() - x.mean()]) ....: Out[78]: <lambda_0> <lambda_1> A bar 0.331279 0.084917 foo 2.337259 -0.215962
New in version 0.25.0.
To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy.agg(), known as “named aggregation”, where
GroupBy.agg()
The keywords are the output column names
The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column. Pandas provides the pandas.NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are. As usual, the aggregation can be a callable or a string alias.
pandas.NamedAgg
['column', 'aggfunc']
In [79]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'], ....: 'height': [9.1, 6.0, 9.5, 34.0], ....: 'weight': [7.9, 7.5, 9.9, 198.0]}) ....: In [80]: animals Out[80]: kind height weight 0 cat 9.1 7.9 1 dog 6.0 7.5 2 cat 9.5 9.9 3 dog 34.0 198.0 In [81]: animals.groupby("kind").agg( ....: min_height=pd.NamedAgg(column='height', aggfunc='min'), ....: max_height=pd.NamedAgg(column='height', aggfunc='max'), ....: average_weight=pd.NamedAgg(column='weight', aggfunc=np.mean), ....: ) ....:  Plain tuples are allowed as well.
namedtuple
In [82]: animals.groupby("kind").agg( ....: min_height=('height', 'min'), ....: max_height=('height', 'max'), ....: average_weight=('weight', np.mean), ....: ) ....: Out[82]: min_height max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75
If your desired output column names are not valid python keywords, construct a dictionary and unpack the keyword arguments
In [83]: animals.groupby("kind").agg(**{ ....: 'total weight': pd.NamedAgg(column='weight', aggfunc=sum), ....: }) ....: Out[83]: total weight kind cat 17.8 dog 205.5
Additional keyword arguments are not passed through to the aggregation functions. Only pairs of (column, aggfunc) should be passed as **kwargs. If your aggregation functions requires additional arguments, partially apply them with functools.partial().
(column, aggfunc)
**kwargs
functools.partial()
For Python 3.5 and earlier, the order of **kwargs in a functions was not preserved. This means that the output column ordering would not be consistent. To ensure consistent ordering, the keys (and so output columns) will always be sorted for Python 3.5.
Named aggregation is also valid for Series groupby aggregations. In this case there’s no column selection, so the values are just the functions.
In [84]: animals.groupby("kind").height.agg( ....: min_height='min', ....: max_height='max', ....: ) ....: Out[84]: min_height max_height kind cat 9.1 9.5 dog 6.0 34.0
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:
aggregate
In [85]: grouped.agg({'C': np.sum, ....: 'D': lambda x: np.std(x, ddof=1)}) ....: Out[85]: C D A bar 0.392940 1.366330 foo -1.796421 0.884785
The function names can also be strings. In order for a string to be valid it must be either implemented on GroupBy or available via dispatching:
In [86]: grouped.agg({'C': 'sum', 'D': 'std'}) Out[86]: C D A bar 0.392940 1.366330 foo -1.796421 0.884785
Some common aggregations, currently only sum, mean, std, and sem, have optimized Cython implementations:
mean
std
sem
In [87]: df.groupby('A').sum() Out[87]: C D A bar 0.392940 1.732707 foo -1.796421 2.824590 In [88]: df.groupby(['A', 'B']).mean() Out[88]: C D A B bar one 0.254161 1.511763 three 0.215897 -0.990582 two -0.077118 1.211526 foo one -0.491888 0.807291 three -0.862495 0.024580 two 0.024925 0.592714
Of course sum and mean are implemented on pandas objects, so the above code would work even without the special versions via dispatching (see below).
The transform method returns an object that is indexed the same (same size) as the one being grouped. The transform function must:
transform
Return a result that is either the same size as the group chunk or broadcastable to the size of the group chunk (e.g., a scalar, grouped.transform(lambda x: x.iloc[-1])).
grouped.transform(lambda x: x.iloc[-1])
Operate column-by-column on the group chunk. The transform is applied to the first group chunk using chunk.apply.
Not perform in-place operations on the group chunk. Group chunks should be treated as immutable, and changes to a group chunk may produce unexpected results. For example, when using fillna, inplace must be False (grouped.transform(lambda x: x.fillna(inplace=False))).
fillna
inplace
False
grouped.transform(lambda x: x.fillna(inplace=False))
(Optionally) operates on the entire group chunk. If this is supported, a fast path is used starting from the second chunk.
For example, suppose we wished to standardize the data within each group:
In [89]: index = pd.date_range('10/1/1999', periods=1100) In [90]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index) In [91]: ts = ts.rolling(window=100, min_periods=100).mean().dropna() In [92]: ts.head() Out[92]: 2000-01-08 0.779333 2000-01-09 0.778852 2000-01-10 0.786476 2000-01-11 0.782797 2000-01-12 0.798110 Freq: D, dtype: float64 In [93]: ts.tail() Out[93]: 2002-09-30 0.660294 2002-10-01 0.631095 2002-10-02 0.673601 2002-10-03 0.709213 2002-10-04 0.719369 Freq: D, dtype: float64 In [94]: transformed = (ts.groupby(lambda x: x.year) ....: .transform(lambda x: (x - x.mean()) / x.std())) ....:
We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check:
# Original Data In [95]: grouped = ts.groupby(lambda x: x.year) In [96]: grouped.mean() Out[96]: 2000 0.442441 2001 0.526246 2002 0.459365 dtype: float64 In [97]: grouped.std() Out[97]: 2000 0.131752 2001 0.210945 2002 0.128753 dtype: float64 # Transformed Data In [98]: grouped_trans = transformed.groupby(lambda x: x.year) In [99]: grouped_trans.mean() Out[99]: 2000 1.168208e-15 2001 1.454544e-15 2002 1.726657e-15 dtype: float64 In [100]: grouped_trans.std() Out[100]: 2000 1.0 2001 1.0 2002 1.0 dtype: float64
We can also visually compare the original and transformed data sets.
In [101]: compare = pd.DataFrame({'Original': ts, 'Transformed': transformed}) In [102]: compare.plot() Out[102]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2d07d38390>
Transformation functions that have lower dimension outputs are broadcast to match the shape of the input array.
In [103]: ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min()) Out[103]: 2000-01-08 0.623893 2000-01-09 0.623893 2000-01-10 0.623893 2000-01-11 0.623893 2000-01-12 0.623893 ... 2002-09-30 0.558275 2002-10-01 0.558275 2002-10-02 0.558275 2002-10-03 0.558275 2002-10-04 0.558275 Freq: D, Length: 1001, dtype: float64
Alternatively, the built-in methods could be used to produce the same outputs.
In [104]: max = ts.groupby(lambda x: x.year).transform('max') In [105]: min = ts.groupby(lambda x: x.year).transform('min') In [106]: max - min Out[106]: 2000-01-08 0.623893 2000-01-09 0.623893 2000-01-10 0.623893 2000-01-11 0.623893 2000-01-12 0.623893 ... 2002-09-30 0.558275 2002-10-01 0.558275 2002-10-02 0.558275 2002-10-03 0.558275 2002-10-04 0.558275 Freq: D, Length: 1001, dtype: float64
Another common data transform is to replace missing data with the group mean.
In [107]: data_df Out[107]: A B C 0 1.539708 -1.166480 0.533026 1 1.302092 -0.505754 NaN 2 -0.371983 1.104803 -0.651520 3 -1.309622 1.118697 -1.161657 4 -1.924296 0.396437 0.812436 .. ... ... ... 995 -0.093110 0.683847 -0.774753 996 -0.185043 1.438572 NaN 997 -0.394469 -0.642343 0.011374 998 -1.174126 1.857148 NaN 999 0.234564 0.517098 0.393534 [1000 rows x 3 columns] In [108]: countries = np.array(['US', 'UK', 'GR', 'JP']) In [109]: key = countries[np.random.randint(0, 4, 1000)] In [110]: grouped = data_df.groupby(key) # Non-NA count in each group In [111]: grouped.count() Out[111]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217 In [112]: transformed = grouped.transform(lambda x: x.fillna(x.mean()))
We can verify that the group means have not changed in the transformed data and that the transformed data contains no NAs.
In [113]: grouped_trans = transformed.groupby(key) In [114]: grouped.mean() # original group means Out[114]: A B C GR -0.098371 -0.015420 0.068053 JP 0.069025 0.023100 -0.077324 UK 0.034069 -0.052580 -0.116525 US 0.058664 -0.020399 0.028603 In [115]: grouped_trans.mean() # transformation did not change group means Out[115]: A B C GR -0.098371 -0.015420 0.068053 JP 0.069025 0.023100 -0.077324 UK 0.034069 -0.052580 -0.116525 US 0.058664 -0.020399 0.028603 In [116]: grouped.count() # original has some missing data points Out[116]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217 In [117]: grouped_trans.count() # counts after transformation Out[117]: A B C GR 228 228 228 JP 267 267 267 UK 247 247 247 US 258 258 258 In [118]: grouped_trans.size() # Verify non-NA count equals group size Out[118]: GR 228 JP 267 UK 247 US 258 dtype: int64
Some functions will automatically transform the input when applied to a GroupBy object, but returning an object of the same shape as the original. Passing as_index=False will not affect these transformation methods.
For example: fillna, ffill, bfill, shift..
fillna, ffill, bfill, shift.
In [119]: grouped.ffill() Out[119]: A B C 0 1.539708 -1.166480 0.533026 1 1.302092 -0.505754 0.533026 2 -0.371983 1.104803 -0.651520 3 -1.309622 1.118697 -1.161657 4 -1.924296 0.396437 0.812436 .. ... ... ... 995 -0.093110 0.683847 -0.774753 996 -0.185043 1.438572 -0.774753 997 -0.394469 -0.642343 0.011374 998 -1.174126 1.857148 -0.774753 999 0.234564 0.517098 0.393534 [1000 rows x 3 columns]
It is possible to use resample(), expanding() and rolling() as methods on groupbys.
resample()
expanding()
rolling()
The example below will apply the rolling() method on the samples of the column B based on the groups of column A.
In [120]: df_re = pd.DataFrame({'A': [1] * 10 + [5] * 10, .....: 'B': np.arange(20)}) .....: In [121]: df_re Out[121]: A B 0 1 0 1 1 1 2 1 2 3 1 3 4 1 4 .. .. .. 15 5 15 16 5 16 17 5 17 18 5 18 19 5 19 [20 rows x 2 columns] In [122]: df_re.groupby('A').rolling(4).B.mean() Out[122]: A 1 0 NaN 1 NaN 2 NaN 3 1.5 4 2.5 ... 5 15 13.5 16 14.5 17 15.5 18 16.5 19 17.5 Name: B, Length: 20, dtype: float64
The expanding() method will accumulate a given operation (sum() in the example) for all the members of each particular group.
In [123]: df_re.groupby('A').expanding().sum() Out[123]: A B A 1 0 1.0 0.0 1 2.0 1.0 2 3.0 3.0 3 4.0 6.0 4 5.0 10.0 ... ... ... 5 15 30.0 75.0 16 35.0 91.0 17 40.0 108.0 18 45.0 126.0 19 50.0 145.0 [20 rows x 2 columns]
Suppose you want to use the resample() method to get a daily frequency in each group of your dataframe and wish to complete the missing values with the ffill() method.
ffill()
In [124]: df_re = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4, .....: freq='W'), .....: 'group': [1, 1, 2, 2], .....: 'val': [5, 6, 7, 8]}).set_index('date') .....: In [125]: df_re Out[125]: group val date 2016-01-03 1 5 2016-01-10 1 6 2016-01-17 2 7 2016-01-24 2 8 In [126]: df_re.groupby('group').resample('1D').ffill() Out[126]: group val group date 1 2016-01-03 1 5 2016-01-04 1 5 2016-01-05 1 5 2016-01-06 1 5 2016-01-07 1 5 ... ... ... 2 2016-01-20 2 7 2016-01-21 2 7 2016-01-22 2 7 2016-01-23 2 7 2016-01-24 2 8 [16 rows x 2 columns]
The filter method returns a subset of the original object. Suppose we want to take only elements that belong to groups with a group sum greater than 2.
filter
In [127]: sf = pd.Series([1, 1, 2, 3, 3, 3]) In [128]: sf.groupby(sf).filter(lambda x: x.sum() > 2) Out[128]: 3 3 4 3 5 3 dtype: int64
The argument of filter must be a function that, applied to the group as a whole, returns True or False.
True
Another useful operation is filtering out elements that belong to groups with only a couple members.
In [129]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')}) In [130]: dff.groupby('B').filter(lambda x: len(x) > 2) Out[130]: A B 2 2 b 3 3 b 4 4 b 5 5 b
Alternatively, instead of dropping the offending groups, we can return a like-indexed objects where the groups that do not pass the filter are filled with NaNs.
In [131]: dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False) Out[131]: A B 0 NaN NaN 1 NaN NaN 2 2.0 b 3 3.0 b 4 4.0 b 5 5.0 b 6 NaN NaN 7 NaN NaN
For DataFrames with multiple columns, filters should explicitly specify a column as the filter criterion.
In [132]: dff['C'] = np.arange(8) In [133]: dff.groupby('B').filter(lambda x: len(x['C']) > 2) Out[133]: A B C 2 2 b 2 3 3 b 3 4 4 b 4 5 5 b 5
Some functions when applied to a groupby object will act as a filter on the input, returning a reduced shape of the original (and potentially eliminating groups), but with the index unchanged. Passing as_index=False will not affect these transformation methods.
For example: head, tail.
head, tail
In [134]: dff.groupby('B').head(2) Out[134]: A B C 0 0 a 0 1 1 a 1 2 2 b 2 3 3 b 3 6 6 c 6 7 7 c 7
When doing an aggregation or transformation, you might just want to call an instance method on each data group. This is pretty easy to do by passing lambda functions:
In [135]: grouped = df.groupby('A') In [136]: grouped.agg(lambda x: x.std()) Out[136]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785
But, it’s rather verbose and can be untidy if you need to pass additional arguments. Using a bit of metaprogramming cleverness, GroupBy now has the ability to “dispatch” method calls to the groups:
In [137]: grouped.std() Out[137]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785
What is actually happening here is that a function wrapper is being generated. When invoked, it takes any passed arguments and invokes the function with any arguments on each group (in the above example, the std function). The results are then combined together much in the style of agg and transform (it actually uses apply to infer the gluing, documented next). This enables some operations to be carried out rather succinctly:
agg
apply
In [138]: tsdf = pd.DataFrame(np.random.randn(1000, 3), .....: index=pd.date_range('1/1/2000', periods=1000), .....: columns=['A', 'B', 'C']) .....: In [139]: tsdf.iloc[::2] = np.nan In [140]: grouped = tsdf.groupby(lambda x: x.year) In [141]: grouped.fillna(method='pad') Out[141]: A B C 2000-01-01 NaN NaN NaN 2000-01-02 -0.353501 -0.080957 -0.876864 2000-01-03 -0.353501 -0.080957 -0.876864 2000-01-04 0.050976 0.044273 -0.559849 2000-01-05 0.050976 0.044273 -0.559849 ... ... ... ... 2002-09-22 0.005011 0.053897 -1.026922 2002-09-23 0.005011 0.053897 -1.026922 2002-09-24 -0.456542 -1.849051 1.559856 2002-09-25 -0.456542 -1.849051 1.559856 2002-09-26 1.123162 0.354660 1.128135 [1000 rows x 3 columns]
In this example, we chopped the collection of time series into yearly chunks then independently called fillna on the groups.
The nlargest and nsmallest methods work on Series style groupbys:
nlargest
nsmallest
In [142]: s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3]) In [143]: g = pd.Series(list('abababab')) In [144]: gb = s.groupby(g) In [145]: gb.nlargest(3) Out[145]: a 4 19.0 0 9.0 2 7.0 b 1 8.0 3 5.0 7 3.3 dtype: float64 In [146]: gb.nsmallest(3) Out[146]: a 6 4.2 2 7.0 0 9.0 b 5 1.0 7 3.3 3 5.0 dtype: float64
Some operations on the grouped data might not fit into either the aggregate or transform categories. Or, you may simply want GroupBy to infer how to combine the results. For these, use the apply function, which can be substituted for both aggregate and transform in many standard use cases. However, apply can handle some exceptional use cases, for example:
In [147]: df Out[147]: A B C D 0 foo one -0.575247 1.346061 1 bar one 0.254161 1.511763 2 foo two -1.143704 1.627081 3 bar three 0.215897 -0.990582 4 foo two 1.193555 -0.441652 5 bar two -0.077118 1.211526 6 foo one -0.408530 0.268520 7 foo three -0.862495 0.024580 In [148]: grouped = df.groupby('A') # could also just call .describe() In [149]: grouped['C'].apply(lambda x: x.describe()) Out[149]: A bar count 3.000000 mean 0.130980 std 0.181231 min -0.077118 25% 0.069390 ... foo min -1.143704 25% -0.862495 50% -0.575247 75% -0.408530 max 1.193555 Name: C, Length: 16, dtype: float64
The dimension of the returned result can also change:
In [150]: grouped = df.groupby('A')['C'] In [151]: def f(group): .....: return pd.DataFrame({'original': group, .....: 'demeaned': group - group.mean()}) .....: In [152]: grouped.apply(f) Out[152]: original demeaned 0 -0.575247 -0.215962 1 0.254161 0.123181 2 -1.143704 -0.784420 3 0.215897 0.084917 4 1.193555 1.552839 5 -0.077118 -0.208098 6 -0.408530 -0.049245 7 -0.862495 -0.503211
apply on a Series can operate on a returned value from the applied function, that is itself a series, and possibly upcast the result to a DataFrame:
In [153]: def f(x): .....: return pd.Series([x, x ** 2], index=['x', 'x^2']) .....: In [154]: s = pd.Series(np.random.rand(5)) In [155]: s Out[155]: 0 0.321438 1 0.493496 2 0.139505 3 0.910103 4 0.194158 dtype: float64 In [156]: s.apply(f) Out[156]: x x^2 0 0.321438 0.103323 1 0.493496 0.243538 2 0.139505 0.019462 3 0.910103 0.828287 4 0.194158 0.037697
apply can act as a reducer, transformer, or filter function, depending on exactly what is passed to it. So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in the output as well as set the indices.
Again consider the example DataFrame we’ve been looking at:
In [157]: df Out[157]: A B C D 0 foo one -0.575247 1.346061 1 bar one 0.254161 1.511763 2 foo two -1.143704 1.627081 3 bar three 0.215897 -0.990582 4 foo two 1.193555 -0.441652 5 bar two -0.077118 1.211526 6 foo one -0.408530 0.268520 7 foo three -0.862495 0.024580
Suppose we wish to compute the standard deviation grouped by the A column. There is a slight problem, namely that we don’t care about the data in column B. We refer to this as a “nuisance” column. If the passed aggregation function can’t be applied to some columns, the troublesome columns will be (silently) dropped. Thus, this does not pose any problems:
In [158]: df.groupby('A').std() Out[158]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785
Note that df.groupby('A').colname.std(). is more efficient than df.groupby('A').std().colname, so if the result of an aggregation function is only interesting over one column (here colname), it may be filtered before applying the aggregation function.
df.groupby('A').colname.std().
df.groupby('A').std().colname
colname
Any object column, also if it contains numerical values such as Decimal objects, is considered as a “nuisance” columns. They are excluded from aggregate functions automatically in groupby.
Decimal
If you do wish to include decimal or object columns in an aggregation with other non-nuisance data types, you must do so explicitly.
In [159]: from decimal import Decimal In [160]: df_dec = pd.DataFrame( .....: {'id': [1, 2, 1, 2], .....: 'int_column': [1, 2, 3, 4], .....: 'dec_column': [Decimal('0.50'), Decimal('0.15'), .....: Decimal('0.25'), Decimal('0.40')] .....: } .....: ) .....: # Decimal columns can be sum'd explicitly by themselves... In [161]: df_dec.groupby(['id'])[['dec_column']].sum() Out[161]: dec_column id 1 0.75 2 0.55 # ...but cannot be combined with standard data types or they will be excluded In [162]: df_dec.groupby(['id'])[['int_column', 'dec_column']].sum() Out[162]: int_column id 1 4 2 6 # Use .agg function to aggregate over standard and "nuisance" data types # at the same time In [163]: df_dec.groupby(['id']).agg({'int_column': 'sum', 'dec_column': 'sum'}) Out[163]: int_column dec_column id 1 4 0.75 2 6 0.55
When using a Categorical grouper (as a single grouper, or as part of multiple groupers), the observed keyword controls whether to return a cartesian product of all possible groupers values (observed=False) or only those that are observed groupers (observed=True).
Categorical
observed
observed=False
observed=True
Show all values:
In [164]: pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'], .....: categories=['a', 'b']), .....: observed=False).count() .....: Out[164]: a 3 b 0 dtype: int64
Show only the observed values:
In [165]: pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'], .....: categories=['a', 'b']), .....: observed=True).count() .....: Out[165]: a 3 dtype: int64
The returned dtype of the grouped will always include all of the categories that were grouped.
In [166]: s = pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'], .....: categories=['a', 'b']), .....: observed=False).count() .....: In [167]: s.index.dtype Out[167]: CategoricalDtype(categories=['a', 'b'], ordered=False)
If there are any NaN or NaT values in the grouping key, these will be automatically excluded. In other words, there will never be an “NA group” or “NaT group”. This was not the case in older versions of pandas, but users were generally discarding the NA group anyway (and supporting it was an implementation headache).
Categorical variables represented as instance of pandas’s Categorical class can be used as group keys. If so, the order of the levels will be preserved:
In [168]: data = pd.Series(np.random.randn(100)) In [169]: factor = pd.qcut(data, [0, .25, .5, .75, 1.]) In [170]: data.groupby(factor).mean() Out[170]: (-2.645, -0.523] -1.362896 (-0.523, 0.0296] -0.260266 (0.0296, 0.654] 0.361802 (0.654, 2.21] 1.073801 dtype: float64
You may need to specify a bit more data to properly group. You can use the pd.Grouper to provide this local control.
In [171]: import datetime In [172]: df = pd.DataFrame({'Branch': 'A A A A A A A B'.split(), .....: 'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(), .....: 'Quantity': [1, 3, 5, 1, 8, 1, 9, 3], .....: 'Date': [ .....: datetime.datetime(2013, 1, 1, 13, 0), .....: datetime.datetime(2013, 1, 1, 13, 5), .....: datetime.datetime(2013, 10, 1, 20, 0), .....: datetime.datetime(2013, 10, 2, 10, 0), .....: datetime.datetime(2013, 10, 1, 20, 0), .....: datetime.datetime(2013, 10, 2, 10, 0), .....: datetime.datetime(2013, 12, 2, 12, 0), .....: datetime.datetime(2013, 12, 2, 14, 0)] .....: }) .....: In [173]: df Out[173]: Branch Buyer Quantity Date 0 A Carl 1 2013-01-01 13:00:00 1 A Mark 3 2013-01-01 13:05:00 2 A Carl 5 2013-10-01 20:00:00 3 A Carl 1 2013-10-02 10:00:00 4 A Joe 8 2013-10-01 20:00:00 5 A Joe 1 2013-10-02 10:00:00 6 A Joe 9 2013-12-02 12:00:00 7 B Carl 3 2013-12-02 14:00:00
Groupby a specific column with the desired frequency. This is like resampling.
In [174]: df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer']).sum() Out[174]: Quantity Date Buyer 2013-01-31 Carl 1 Mark 3 2013-10-31 Carl 6 Joe 9 2013-12-31 Carl 3 Joe 9
You have an ambiguous specification in that you have a named index and a column that could be potential groupers.
In [175]: df = df.set_index('Date') In [176]: df['Date'] = df.index + pd.offsets.MonthEnd(2) In [177]: df.groupby([pd.Grouper(freq='6M', key='Date'), 'Buyer']).sum() Out[177]: Quantity Date Buyer 2013-02-28 Carl 1 Mark 3 2014-02-28 Carl 9 Joe 18 In [178]: df.groupby([pd.Grouper(freq='6M', level='Date'), 'Buyer']).sum() Out[178]: Quantity Date Buyer 2013-01-31 Carl 1 Mark 3 2014-01-31 Carl 9 Joe 18
Just like for a DataFrame or Series you can call head and tail on a groupby:
In [179]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) In [180]: df Out[180]: A B 0 1 2 1 1 4 2 5 6 In [181]: g = df.groupby('A') In [182]: g.head(1) Out[182]: A B 0 1 2 2 5 6 In [183]: g.tail(1) Out[183]: A B 1 1 4 2 5 6
This shows the first or last n rows from each group.
To select from a DataFrame or Series the nth item, use nth(). This is a reduction method, and will return a single row (or no row) per group if you pass an int for n:
In [184]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) In [185]: g = df.groupby('A') In [186]: g.nth(0) Out[186]: B A 1 NaN 5 6.0 In [187]: g.nth(-1) Out[187]: B A 1 4.0 5 6.0 In [188]: g.nth(1) Out[188]: B A 1 4.0
If you want to select the nth not-null item, use the dropna kwarg. For a DataFrame this should be either 'any' or 'all' just like you would pass to dropna:
dropna
'any'
'all'
# nth(0) is the same as g.first() In [189]: g.nth(0, dropna='any') Out[189]: B A 1 4.0 5 6.0 In [190]: g.first() Out[190]: B A 1 4.0 5 6.0 # nth(-1) is the same as g.last() In [191]: g.nth(-1, dropna='any') # NaNs denote group exhausted when using dropna Out[191]: B A 1 4.0 5 6.0 In [192]: g.last() Out[192]: B A 1 4.0 5 6.0 In [193]: g.B.nth(0, dropna='all') Out[193]: A 1 4.0 5 6.0 Name: B, dtype: float64
As with other methods, passing as_index=False, will achieve a filtration, which returns the grouped row.
In [194]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) In [195]: g = df.groupby('A', as_index=False) In [196]: g.nth(0) Out[196]: A B 0 1 NaN 2 5 6.0 In [197]: g.nth(-1) Out[197]: A B 1 1 4.0 2 5 6.0
You can also select multiple rows from each group by specifying multiple nth values as a list of ints.
In [198]: business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B') In [199]: df = pd.DataFrame(1, index=business_dates, columns=['a', 'b']) # get the first, 4th, and last date index for each month In [200]: df.groupby([df.index.year, df.index.month]).nth([0, 3, -1]) Out[200]: a b 2014 4 1 1 4 1 1 4 1 1 5 1 1 5 1 1 5 1 1 6 1 1 6 1 1 6 1 1
To see the order in which each row appears within its group, use the cumcount method:
cumcount
In [201]: dfg = pd.DataFrame(list('aaabba'), columns=['A']) In [202]: dfg Out[202]: A 0 a 1 a 2 a 3 b 4 b 5 a In [203]: dfg.groupby('A').cumcount() Out[203]: 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 In [204]: dfg.groupby('A').cumcount(ascending=False) Out[204]: 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64
To see the ordering of the groups (as opposed to the order of rows within a group given by cumcount) you can use ngroup().
ngroup()
Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed.
In [205]: dfg = pd.DataFrame(list('aaabba'), columns=['A']) In [206]: dfg Out[206]: A 0 a 1 a 2 a 3 b 4 b 5 a In [207]: dfg.groupby('A').ngroup() Out[207]: 0 0 1 0 2 0 3 1 4 1 5 0 dtype: int64 In [208]: dfg.groupby('A').ngroup(ascending=False) Out[208]: 0 1 1 1 2 1 3 0 4 0 5 1 dtype: int64
Groupby also works with some plotting methods. For example, suppose we suspect that some features in a DataFrame may differ by group, in this case, the values in column 1 where the group is “B” are 3 higher on average.
In [209]: np.random.seed(1234) In [210]: df = pd.DataFrame(np.random.randn(50, 2)) In [211]: df['g'] = np.random.choice(['A', 'B'], size=50) In [212]: df.loc[df['g'] == 'B', 1] += 3
We can easily visualize this with a boxplot:
In [213]: df.groupby('g').boxplot() Out[213]: A AxesSubplot(0.1,0.15;0.363636x0.75) B AxesSubplot(0.536364,0.15;0.363636x0.75) dtype: object
The result of calling boxplot is a dictionary whose keys are the values of our grouping column g (“A” and “B”). The values of the resulting dictionary can be controlled by the return_type keyword of boxplot. See the visualization documentation for more.
boxplot
g
return_type
Warning
For historical reasons, df.groupby("g").boxplot() is not equivalent to df.boxplot(by="g"). See here for an explanation.
df.groupby("g").boxplot()
df.boxplot(by="g")
New in version 0.21.0.
Similar to the functionality provided by DataFrame and Series, functions that take GroupBy objects can be chained together using a pipe method to allow for a cleaner, more readable syntax. To read about .pipe in general terms, see here.
pipe
.pipe
Combining .groupby and .pipe is often useful when you need to reuse GroupBy objects.
.groupby
As an example, imagine having a DataFrame with columns for stores, products, revenue and quantity sold. We’d like to do a groupwise calculation of prices (i.e. revenue/quantity) per store and per product. We could do this in a multi-step operation, but expressing it in terms of piping can make the code more readable. First we set the data:
In [214]: n = 1000 In [215]: df = pd.DataFrame({'Store': np.random.choice(['Store_1', 'Store_2'], n), .....: 'Product': np.random.choice(['Product_1', .....: 'Product_2'], n), .....: 'Revenue': (np.random.random(n) * 50 + 10).round(2), .....: 'Quantity': np.random.randint(1, 10, size=n)}) .....: In [216]: df.head(2) Out[216]: Store Product Revenue Quantity 0 Store_2 Product_1 26.12 1 1 Store_2 Product_1 28.86 1
Now, to find prices per store/product, we can simply do:
In [217]: (df.groupby(['Store', 'Product']) .....: .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum()) .....: .unstack().round(2)) .....: Out[217]: Product Product_1 Product_2 Store Store_1 6.82 7.05 Store_2 6.30 6.64
Piping can also be expressive when you want to deliver a grouped object to some arbitrary function, for example:
In [218]: def mean(groupby): .....: return groupby.mean() .....: In [219]: df.groupby(['Store', 'Product']).pipe(mean) Out[219]: Revenue Quantity Store Product Store_1 Product_1 34.622727 5.075758 Product_2 35.482815 5.029630 Store_2 Product_1 32.972837 5.237589 Product_2 34.684360 5.224000
where mean takes a GroupBy object and finds the mean of the Revenue and Quantity columns respectively for each Store-Product combination. The mean function can be any function that takes in a GroupBy object; the .pipe will pass the GroupBy object as a parameter into the function you specify.
Regroup columns of a DataFrame according to their sum, and sum the aggregated ones.
In [220]: df = pd.DataFrame({'a': [1, 0, 0], 'b': [0, 1, 0], .....: 'c': [1, 0, 0], 'd': [2, 3, 4]}) .....: In [221]: df Out[221]: a b c d 0 1 0 1 2 1 0 1 0 3 2 0 0 0 4 In [222]: df.groupby(df.sum(), axis=1).sum() Out[222]: 1 9 0 2 2 1 1 3 2 0 4
By using ngroup(), we can extract information about the groups in a way similar to factorize() (as described further in the reshaping API) but which applies naturally to multiple columns of mixed type and different sources. This can be useful as an intermediate categorical-like step in processing, when the relationships between the group rows are more important than their content, or as input to an algorithm which only accepts the integer encoding. (For more information about support in pandas for full categorical data, see the Categorical introduction and the API documentation.)
factorize()
In [223]: dfg = pd.DataFrame({"A": [1, 1, 2, 3, 2], "B": list("aaaba")}) In [224]: dfg Out[224]: A B 0 1 a 1 1 a 2 2 a 3 3 b 4 2 a In [225]: dfg.groupby(["A", "B"]).ngroup() Out[225]: 0 0 1 0 2 1 3 2 4 1 dtype: int64 In [226]: dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup() Out[226]: 0 0 1 0 2 1 3 3 4 2 dtype: int64
Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
In order to resample to work on indices that are non-datetimelike, the following procedure can be utilized.
In the following examples, df.index // 5 returns a binary array which is used to determine what gets selected for the groupby operation.
The below example shows how we can downsample by consolidation of samples into fewer samples. Here by using df.index // 5, we are aggregating the samples in bins. By applying std() function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation thereby reducing the number of samples.
In [227]: df = pd.DataFrame(np.random.randn(10, 2)) In [228]: df Out[228]: 0 1 0 -0.793893 0.321153 1 0.342250 1.618906 2 -0.975807 1.918201 3 -0.810847 -1.405919 4 -1.977759 0.461659 5 0.730057 -1.316938 6 -0.751328 0.528290 7 -0.257759 -1.081009 8 0.505895 -1.701948 9 -1.006349 0.020208 In [229]: df.index // 5 Out[229]: Int64Index([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype='int64') In [230]: df.groupby(df.index // 5).std() Out[230]: 0 1 0 0.823647 1.312912 1 0.760109 0.942941
Group DataFrame columns, compute a set of metrics and return a named Series. The Series name is used as the name for the column index. This is especially useful in conjunction with reshaping operations such as stacking in which the column index name will be used as the name of the inserted column:
In [231]: df = pd.DataFrame({'a': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], .....: 'b': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1], .....: 'c': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0], .....: 'd': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]}) .....: In [232]: def compute_metrics(x): .....: result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()} .....: return pd.Series(result, name='metrics') .....: In [233]: result = df.groupby('a').apply(compute_metrics) In [234]: result Out[234]: metrics b_sum c_mean a 0 2.0 0.5 1 2.0 0.5 2 2.0 0.5 In [235]: result.stack() Out[235]: a metrics 0 b_sum 2.0 c_mean 0.5 1 b_sum 2.0 c_mean 0.5 2 b_sum 2.0 c_mean 0.5 dtype: float64


org.apache.commons.rng.sampling
Classes
CollectionSampler
CombinationSampler
DiscreteProbabilityCollectionSampler
ListSampler
PermutationSampler
SubsetSamplerUtils
UnitSphereSampler


Invoice (Sample for Calculation)
Navigation Controls by INTER-Mediator
id
issued
title
product
qty
product_name
unitprice
net price
tax price
amount
Tax Rate:
Total:


Audi
Audi AG is a German automobile manufacturer that designs, engineers, produces, markets and distributes luxury vehicles. Audi is a member of the Volkswagen Group and has its roots at Ingolstadt, Bavaria, Germany. Audi-branded vehicles are produced in nine production facilities worldwide.
The origins of the company are complex, going back to the early 20th century and the initial enterprises (Horch and the Audiwerke) founded by engineer August Horch; and two other manufacturers (DKW and Wanderer), leading to the foundation of Auto Union in 1932. The modern era of Audi essentially began in the 1960s when Auto Union was acquired by Volkswagen from Daimler-Benz. After relaunching the Audi brand with the 1965 introduction of the Audi F103 series, Volkswagen merged Auto Union with NSU Motorenwerke in 1969, thus creating the present day form of the company.
The company name is based on the Latin translation of the surname of the founder, August Horch. "Horch", meaning "listen" in German, becomes "audi" in Latin. The four rings of the Audi logo each represent one of four car companies that banded together to create Audi's predecessor company, Auto Union. Audi's slogan is Vorsprung durch Technik, meaning "Being Ahead through Technology". However, Audi USA had used the slogan "Truth in Engineering" from 2007 to 2016, and have not used the slogan since 2016. Audi, along with fellow German marques BMW and Mercedes-Benz, is among the best-selling luxury automobile brands in the world.
BMW
BMW AG originally an initialism for Bayerische Motoren Werke in German, or Bavarian Motor Works in English) is a German multinational company which currently produces luxury automobiles and motorcycles, and also produced aircraft engines until 1945.
The company was founded in 1916 and has its headquarters in Munich, Bavaria. BMW produces motor vehicles in Germany, Brazil, China, India, South Africa, the United Kingdom, and the United States. In 2015, BMW was the world’s twelfth largest producer of motor vehicles, with 2,279,503 vehicles produced. The Quandt family are long-term shareholders of the company, with the remaining shares owned by public float.
Automobiles are marketed under the brands BMW (with sub-brands BMW M for performance models and BMW i for plug-in electric cars), Mini and Rolls-Royce. Motorcycles are marketed under the brand BMW Motorrad.
The company has significant motorsport history, especially in touring cars, Formula 1, sports cars and the Isle of Man TT.
Volkswagen
Volkswagen, shortened to VW, is a German automaker founded on 28 May 1937 by the German Labour Front under Robert Ley and headquartered in Wolfsburg. It is the flagship marque of the Volkswagen Group, the largest automaker by worldwide sales in 2016 and 2017. The group's main market is in China, which delivers 40% of its sales and profits.
Volkswagen translates to "folk's wagon" in German, meaning "people's car". The company's current international advertising slogan is just "Volkswagen", referencing the name's meaning.
Volkswagen was originally established in 1932 by the German Labour Front (Deutsche Arbeitsfront) in Berlin. In the early 1930s cars were a luxury: most Germans could afford nothing more elaborate than, at most, a motorcycle. As a result, only one German out of 50 owned a car. Seeking a potential new market, some car makers began independent "people's car" projects – the Mercedes 170H, Adler AutoBahn, Steyr 55, and Hanomag 1.3L, among others.


This is a blank page that loads the gdc.js script. All the action is in the browser's command line.
To load the same library in your webApp just include this line (there are NO dependencies from other libraries, such as jQuery):
<script src="https://mathbiol.github.io/gdc/gdc.js"></script>


defKPI01
defKPI03
Final SDTM
defKPI09
DB Transfer
defKPI02
defKPI03
ADaM/ADS
defKPI02
Submission Package
Content Checklist
defKPI09
Final ADaM/ADS
defKPI09
Submission Last Study
Decision Timepoint
Handover To Regulatory
Department Timepoint
defKPI03
SDTM
Regulatory Agency
Submission Package
Content Agreement Timepoint
PhUSE Data Science Metric Reporting & KPI Project
PhUSE KPI Terminology
Definition Template (pdf)
PhUSE KPI Metric
Defintion Template (pdf)
CDISC Glossary
CDISC Acronyms
Metrics-Condensed 12-Nov-16
[AR kpi-v02]
GitHub KPI Template
defKPI05


{{Infobox prepared food | name = Bánh khoai mì | image = | caption = | alternate_name = | country = Vietnam | region = | creator = | course = | type = Cake | served = | main_ingredient = Cassava, sugar, coconut milk | variations = Bánh khoai mì nướng, Bánh khoai mì hấp | calories = | other = }}
Bánh khoai mì is a Vietnamese cake made from grated cassava, sugar, coconut milk, and a small amount of salt.<ref>Lucy Nguyen-Hong-Nhiem - A Dragon Child: Reflections Of A Daughter Of Annam In America Page 13 2004 "She loved to cook our favorite dishes, bánh xèo and bánh khoái."</ref>
There are two varieties:
A similar cake, made from taro, is called bánh khoai môn.
{{reflist}}
{{Vietnam-stub}} {{dessert-stub}}
{{DEFAULTSORT:Banh khoai mi}}


Article Examples
Easy to read news articles updated daily
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim.
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
Lorem ipsum dolor sit amet, con adipiscing elit, sed do eiusmod tempor incint ut labore et dolore magna aliqua. Ut enim ad minim. veniam, quiud exercition ull
NY Deal Report is a news and data company that translates difficult to navigate public records in New York City into articles and usable data.
Deed Transfers
Partnership Buyouts
Complex Transactions
Land Leases
Refinances
Construction Loans
Loan Modifications
New Mortgages
Debt Sales
Alt 1 Permits
Alt 2 Permits
Demolition Permits
New Building Permits
Sale Date
Sale Price
Property Type
Property Address & Description
Mortgage Amount & Lender
Buyer & Seller Name
& Much More
All types of transactions
Elevator Buildings
Walk Up Buildings
2 & 3 Families
Conversions
Retail & Residential
Retail & Office
Pharmacies
Fast Food
Gas Stations
Auto
Banks
Taxpayers
Shopping Centers
Office Buildings
Medical Office Buildings
Single Tenant Medical Buildings
Warehouses
Auto Body Shops
Fulfillment Centers
Flex Space
New Development
Vacant Land
Teardowns
Vacant Retail with Air Rights
Vacant Warehouses with Air Rights
Vacant Auto Repair Shops with Air Rights
Bulk Condo
Retail Condos
Condo Buildings
Net Leased Schools
Day Cares
Hotels
Self-Storage
& Much More!
Easy to read news articles updated daily
Abraham Chetrit purchased a 14-unit, 6,825 square foot multifamily building located at 506 E 6th St in the East Village section of Manhattan for $3,950,000. The price is equal to $579/ft and $282,143/unit. The Seller, Michael Catsimalis, has owned the building since the 1980’s.
Chetrit obtained a mortgage for $2,100,000 from Signature bank to finance the transaction. The deal went into contract and closed on 08/24/20 and was recorded on 08/25/20.
Albert Shirian’s Lions Group borrowed $19,100,000 from Popular Bank to refinance a new development mixed use property located at 227-229 Lexington Ave in Kips Bay, Manhattan. The property was purchased for $3,500,000 in January 2015 and developed into a 37 unit mixed use building.
The financing included $3,400,000 in new debt in the form of a gap loan on top of the outstanding loan balance of $15,700,000 from the old mortgage.
The loan closed on 7/31/2020 and was recorded on 8/11/2020.
Mark Levin’s Hirshmark Capital purchased $7,100,000 in debt on a Hell’s Kitchen condo project located at 805 9th Ave from Greenwich, CT based Knighthead Funding. The mortgagors (borrowers), Hashem Dardashtian, Israel Abselet, Daniel Rahimzadeh, and Jon Rahimzadeh, purchased the parcel for $4,000,000 in 2014. On 8/25/2016 they had submitted plans to build a 6-unit, 10,455 square foot condominium building with a proposed sellout price of $18,430,000. The plans call for 1 commercial and 5 residential units.
The debt is comprised of a $5,750,000 land loan and a $1,350,000 construction loan. The deal closed on 6/12/2020 and was recorded on 8/11/2020.
Jason Behfarin of 240 Broadway Property LLC, was issued an Alt1 permit to change the use of floors 2-5 of 240 Broadway in Williamsburg. Currently the building contains retail on the ground floor (2 stores) with 24 residential units above. Behfarin will be renovating the existing retail on the ground floor and changing the use on floors 2-5 from residential to office. The building is owned by both Jason Behfarin of G4 Capital Partners and Danny Zelouf.
The estimated total cost of the job is $2,000,000. The job was pre-filed in 3/2/2020 and the permit was issued on 7/30/2020.
Ruben Luna, through the entity 2182 W.P.R. Realty LLC, purchased a single story retail taxpayer located at 2182 White Plains Rd in the Pelham Parkway neighborhood of the Bronx. The building is located just south of the Pelham Pkwy 2 & 5 subway line.
Josephine Sperazza has owned the building since the late 1970’s. Her son Michael was the signatory on the deed.
Luna did not obtain a mortgage to finance the transaction. The deal went into contract on 6/12/2020, closed on 8/7/2020, and was recorded on 8/13/2020.
Real Estate Sales and Loans Data delivered in real time
| Sale Date | Address | Neighborhood | Borough | Property Type | Building SF | Sale price | Price/sqft | Buyer Name | Buyer Name |
|---|---|---|---|---|---|---|---|---|---|
| 8/28/20 | 90-06 Park Lane S | Woodhaven | Queens | Multifamily | 9,135 | $2,300,000 | $252 | Coy Gordon | Gunther Day |
| 8/20/20 | 116-01 Rockaway Beach Blvd | Rockaway Park | Queens | Retail | 5,240 | $1,739,000 | $332 | Alex Adjmi | Jennifer Windbeck |
| 8/7/20 | 5623 4th Ave | Sunset Park | Brooklyn | Mixed Use | 8,556 | $2,100,000 | $245 | Yong Xiang LI | Maybelliene Ruotolo |
| 8/5/20 | 1705 Jerome Ave | Morris Heights | Bronx | Mixed Use | 9,900 | $1,993,534 | $201 | Manny Chadha | Louis Zazzarino |
| 7/28/20 | 2105 Walton Avenue | Fordham Heights | Bronx | Multifamily | 51,165 | $7,000,000 | $137 | Rick Herman | Martin Kirzner |
| 8/12/20 | 533-537 Park Ave | Bedford Stuyvesant | Brooklyn | Development Site | 13,557 | $5,800,000 | $427 | Cheskie Weisz | Thomas Hussey |
| Thomas Hussey | 506 E 6th St | East Village | Manhattan | Multifamily | 6,825 | $3,950,000 | $579 | Abraham Chetrit | Michael Catsimalis |
| 7/28/20 | 319 E 75th St | Lenox Hill | Manhattan | Multifamily | 11,105 | $5,250,000 | $472 | Tarek Alam & Thomas BiFalco | Steven Cohen |
| 7/8/20 | 82-10 Baxter Avenue | Elmhurst | Queens | Retail | 24,000 | $15,000,000 | $625 | Mohammad Mujalli | Chandra Nisthalal |
| Sale Date | Address | Neighborhood | Borough | Property Type | Building SF | Sale price | Price/sqft | Buyer Name | Buyer Name |
|---|---|---|---|---|---|---|---|---|---|
| 8/28/20 | 90-06 Park Lane S | Woodhaven | Queens | Multifamily | 9,135 | $2,300,000 | $252 | Coy Gordon | Gunther Day |
| 8/20/20 | 116-01 Rockaway Beach Blvd | Rockaway Park | Queens | Retail | 5,240 | $1,739,000 | $332 | Alex Adjmi | Jennifer Windbeck |
| 8/7/20 | 5623 4th Ave | Sunset Park | Brooklyn | Mixed Use | 8,556 | $2,100,000 | $245 | Yong Xiang LI | Maybelliene Ruotolo |
| 8/5/20 | 1705 Jerome Ave | Morris Heights | Bronx | Mixed Use | 9,900 | $1,993,534 | $201 | Manny Chadha | Louis Zazzarino |
| 7/28/20 | 2105 Walton Avenue | Fordham Heights | Bronx | Multifamily | 51,165 | $7,000,000 | $137 | Rick Herman | Martin Kirzner |
| 8/12/20 | 533-537 Park Ave | Bedford Stuyvesant | Brooklyn | Development Site | 13,557 | $5,800,000 | $427 | Cheskie Weisz | Thomas Hussey |
| Thomas Hussey | 506 E 6th St | East Village | Manhattan | Multifamily | 6,825 | $3,950,000 | $579 | Abraham Chetrit | Michael Catsimalis |
| 7/28/20 | 319 E 75th St | Lenox Hill | Manhattan | Multifamily | 11,105 | $5,250,000 | $472 | Tarek Alam & Thomas BiFalco | Steven Cohen |
| 7/8/20 | 82-10 Baxter Avenue | Elmhurst | Queens | Retail | 24,000 | $15,000,000 | $625 | Mohammad Mujalli | Chandra Nisthalal |
“Ny Deal Report has been a great resource for seeing market insight & deals that normally would not be shared to the greater market. They definitely help fuel the market & have their pulse on what’s going on in today’s real estate market. Will definitely continue following them more knowledge & insight.”
- Josh BanilivyI know I can rely on @NyDealReport to deliver up to date information on closings and refinances in the New York Marketplace. It's fantastic getting updates throughout the day while at work. It is great to have a smart, fun and accessible resource at your fingertips.”
- Yoel SarrafNY Deal Report is a trustworthy investment research source and a formidable tool in the arsenal of every market participant. Their team cuts through opaque public records to get you the information that matters most.”
- Josh Banilivy“Ny Deal Report has been a great resource for seeing market insight & deals that normally would not be shared to the greater market. They definitely help fuel the market & have their pulse on what’s going on in today’s real estate market. Will definitely continue following them more knowledge & insight.”
- Josh BanilivyCopyright © 2020 Nydealreport.com. All rights reserved.


|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current version: 1.0.1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Character universe introduction
The character universe is the central topic to understanding how and why regldg does what it does with character and meta-character classes. The character universe is the group of characters that regldg is allowed to use to make the words of the output dictionary. Lets look at an example. Given the regular expression . , what do you expect the output of regldg to be? Well, it is clear that each word output will be only one character long, but which characters will those be? Could they be only uppercase letters? Could they include lowercase letters too? Numbers? Other symbols on the keyboard? Or, maybe they could be all the possible characters from the ASCII and extended ASCII character sets (0-255)? The set of possible characters is called the character universe, and you can decide what it should be for each run of regldg. There are two command line options to set the character universe. Firstly, there are a number of common, pre-defined character universe sets. These are:
1. Uppercase letters A-Z To use any one of these pre-defined character universe sets, specify the -us NNN or --universe-set=NNN option on the command line, where NNN is the number of the universe set. You've probably noticed that the number of each pre-defined character universe set is a power of 2. This is so that you can combine universe sets simply by adding their numbers. If you want the character universe to have letters (upper- and lowercase), numbers, and punctuation, you can specify universe set number 23 (1 + 2 + 4 + 16). The second way to specify the character universe is explicitly in a character class. You can put whatever characters you'd like in it using the formats shown in regldg's regular expression capabilities. You can specify the character class on the command line with option -u [UNIVERSE] or --universe=[UNIVERSE]. Be sure to start the character class with [ and end with ]. It will be parsed exactly like a character class, so if you make it a negated character class, it will be negated from the default character universe (universe set 7), or, if you already specified a different universe on the command line with -u or -us, it will be negated from that universe. Here's an example: use regldg to generate all possible combinations of two-letter words using only the characters A, B, and C.
To show that regldg is not afraid to be complex, let's do the same thing using the negated character class method. First, we set the character universe to uppercase letters. Then, we take out D-Z (leaving A-C). Finally, using the remaining characters, we output all possible two-letter words.
While parsing a regular expression, there are two areas where the character universe must be controlled. regldg allows the control in both of these areas to be strict (on) or lax (off) for each run, determined by a single command line option. The first area where controlling the character universe is important is in the explicit entry of characters. If the character universe is set to the uppercase letters A-Z, and a regular expression contains a space, should it result in an error (strict), or should it be allowed in only that place (lax)? This can be controlled by using the command line option -uc N or --universe-checking=N. Setting this value to 1 will enable strict checking of explicitly entered characters. The second area where controlling the character universe is important is character and meta-character classes. If the character universe is set to the digits 0-4 only, and a regular expression contains a \d meta-character, should the resulting character class contain only the digits 0-4 (strict), or should it contain all the digits according to the full specification of \d (lax)? This behavior can also be controlled by using the command line option -uc N or --universe-checking=N. Setting this value to 2 will enable strict checking of the contents of character and meta-character classes. To enable both types of strictness, 1 + 2 = 3, so set -uc 3 or --universe-checking=3. To disable both types of strictness, use -uc 0 or --universe-checking=0. Lets see these in action with an example. If you are using a character universe of only the letters A-E, and generating all possible words, but you want each word of output to start with Z, you'd like to be able to use regldg -u "[ABCDE]" "Z.*". Here we go:
It didn't work! regldg got angry because we tried to use Z, but Z isn't in the character universe [ABCDE]. Ok, so lets turn off universe checking and see what happens.
Hmmm. Unexpected results! And wonderful system bells! Lets turn on --readable-output to see what happened.
It did allow us to start words with Z, which wasn't in the character universe, but why are we getting ASCII characters starting from 0? The problem is the . metacharacter. As explained above, regldg allows you to make metacharacters retain all their characters (lax checking), or have the classes they represent thinned according to the current character universe (strict checking). (Technically: the character or meta-character class can be intersected with the character universe.) In the above example, the . metacharacter was allowed to represent all ASCII values 0-255, so we didn't get only the expected ZA, ZB, and ZC. Since we want . to represent only those charaters in the current character universe, we should turn on this type of strict character universe checking by adding a 2 to the --universe-checking option. So, using this information:
All set! That is what we wanted.
|


Gordon Ramsey's scrambled eggs
In a cold saucepan or small pot with a single handle, crack three eggs and add some salted butter, about a tablespoon per egg. This isnt meant to be had everyday. Over a medium flame stir constantly, occasionally removing the pan from the heat still stirring. the result youd be looking for is a nice nice balance of fluffy and creamy. right before this point remove from heat and stir in an optional creme fraich cooking with residual heat, add chives and serve with roasted mushrooms and cherry tomatoes on the vine. Ideally placed on toast, I gravitate towards rye or whole grain.
CLick the button to wait 3 seconds, then alert "Intruder Alert".


A command-line tool to end my bitmap font generation needs for-friggin-ever. I hope. :)
| -h, --help | This help |
| -dpi # | DPI for font rendering (72 is default) |
| -bpp # | Bpp of bitmap files (8, 24, 32, default is 8) |
| -bold [#] | Generate a bold font (# is factor, default 20) |
| -italic [#] | Generate an italics font (# is factor, default 20) |
| -chars "chars" | Add specified chars to charset |
| -max # | Maximum texture size (power of 2, default is 1024) |
| -noASCII | Do not include the 32-127 chars by default |
| -cp "codepage" | Codepage for translating 8-bit characters |
FontCreator C:\Windows\Fonts\ARIALUNI.TTF 80 "My Fonts\ArialNumsIt" -max 512 -noASCII -chars "0123456789" -italic -bold 30That will generate files:
I use the Freetype library for font rendering, it might support formats other than TTF but I haven't tried them.
Use double quotes around parameters that contain spaces. Especially important for filenames.
For font sizes and DPI settings, I have followed Photoshop's conventions.
Bold and italics are "fake" deformations of the character bitmap; for the real effects you need separate source font files. The optional parameter supplied to -italic and -bold are a percentage of the total character width, and can be negative to invert the effect. A bold parameter of -100 can't be used.
Charset files are regular text files, either classic 8-bit characters or 16-bit Unicode (UCS-2 Big Endian or Little Endian). You can add as many text files as you want. Use -noASCII if you only want characters you supply directly (via -chars or via charset files). FontCreator will not duplicate characters in the resulting font regardless where they come from.
Codepages are... well, it's a long and confusing topic. Ignore it and it will use the system defaults. Use -cp "C" to avoid any translation. In Windows, you can use -cp ".1252" (notice the leading dot) for the Latin-1 codepage, -cp ".1255" for Hebrew, etc. You can find a list of Windows codepages at http://www.microsoft.com/typography/unicode/cscp.htm or http://msdn.microsoft.com/library/default.asp?url=/library/en-us/intl/unicode_81rn.asp, and more info in general at http://en.wikipedia.org/wiki/Codepage. I have no idea how this works in other operating systems. Codepage settings are active at the point where they are specified, so if you have an 8-bit charset file called "file1.txt" and write:
FontCreator ... -cp ".437" file1.txt -cp ".850" file1.txt
then the charset file will be read twice, and interpreted differently each time. If you have any trouble, don't use -cp and supply Unicode charset files instead.
The TFN file is a plain text file where you can find the the list of texture files and the parameters for each character in the font.
Unicode advance offsetX offsetY TextureIndex TextureU TextureV Width Height
The Unicode is hexadecimal (for example, 0x0020 would be the space ' '). Everything else should be pretty much self-explanatory. To draw a character, draw the rectangle (TextureU, TextureV, Width, Height) from the TextureIndex texture, at the position (X + offsetX, Y + offsetY), then add "advance" to X, repeat this for each character in your string. When going to the next line, just add the font's "height" to Y.
The TFN file is deliberately minimalistic in structure. It is designed like that to minimize loading complexity. It contains the texture dimensions as a helper in case you later postprocess the TGAs and change their size - you need the original dimensions in order to interpret the TextureU and TextureV of the characters.
The TGA files are saved as follows:


At Mindera we use technology to build products we are proud of, with people we love. Software Engineering Applications, including Web and Mobile, are at the core of what we do at Mindera. We partner with our clients, to understand their product and deliver high performance, resilient and scalable software systems that create an impact in their users and businesses across the world. You get to work with a bunch of great people, where the whole team owns the project together. Our culture reflects our lean and self management attitude. We encourage our colleagues to take risks, make decisions, work in a collaborative way and talk to everyone to enhance communication. We are proud of our work and we love to learn all and everything while navigating through an Agile, Lean and collaborative environment.
Mobile Craftsman at Mindera, but overall an "improver".  Well, he thrives by (trying until he successfully) improves the processes around him, either “human" or software related. He has been developing mobile apps since 2009, and losing hair almost since then. Loves good talks, code and pranks (if possible at the same time).
Graduate Product Owner at Mindera. Must of all, I love challenges, so after 2 years working in Mindera’s Operations team, I decided to take a new challenge, as a product owner. I love a good environment, a great place to work, to be alway at a place you can call home. For that, I truly believe, one must be an active part of creating better ecosystems.


I’m a multidisciplinary product designer for Mobile and Web applications. I’ve spent 8 years as a Quality Assurance Engineer for music applications, fighting for the user experience (One of which inludes Apple’s 2013 iPad App of the Year). Since 2013, I have shifted my focus onto designing a great User Experience.
With combined business knowledge gained while building 2 startups, my multidisciplinary computing-arts degree, and experience in the tech space, I am equipped to improve your existing UX and UI design, or create and transform your ideas into beautifully visible, tangible, and testable designs and prototypes, ready for development, or manage the development process all the way through to deployment.


#include <string_ops.h>
Check if the input matches the regex pattern.
The input is a string tensor of any shape. The pattern is a scalar string tensor which is applied to every element of the input tensor. The boolean values (True or False) of the output tensor indicate if the input matches the regex pattern provided.
The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
Arguments:
Returns:
Output
: A bool tensor with the same shape as input
.
Constructors and Destructors |
|
|---|---|
RegexFullMatch(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input pattern)
|
Public attributes |
|
|---|---|
operation
|
|
output
|
Public functions |
|
|---|---|
node() const
|
::tensorflow::Node *
|
operator::tensorflow::Input() const
|
|
operator::tensorflow::Output() const
|
|
Operation operation
::tensorflow::Output output
RegexFullMatch( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input pattern )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const


THE ASSIGNMENT Cast: Aidan Quinn, Donald Sutherland, Ben Kingsley, Claudia Ferri, Celine Bonnier, Vlasta Vrana, Liliana Komorowska Director: Christian Duguay Screenplay: Dan Gordon, Sabi H. Shabtai Running time: 119 minutes Australian distributor: Columbia Tristar Australian theatrical release: May 14, 1998 Reviewed by Luke Buckmaster
On the Buckmaster scale of 0 stars (bomb), to 5 stars (a masterpiece): 4 stars
In this smart and stylish action thriller, the life and meaning of Carlos "The Jackal" Sanchez is given new depth. Of the many gratifying qualities that The Assignment has to offer, none are more dazzling than the film's editing and direction. Christian Duguay (Screamers) shapes its screenplay with appropriate complexity, giving his material more than just charm and intrigue. Clash of cultures, moving emotional elements and investigations of political espionage wrap themselves around B grade action, making an A grade experience.
For twenty years, CIA operative Jack Shaw (Donald Sutherland, who has been in a somewhat disturbing amount of films - eighty or so in the last thirty years) has chased Sanchez, a master terrorist who enjoys killing/maiming innocent civilians. After countless failed attempts to capture the ingenious killer, Shaw and his Israeli counterpart Amos (Ben Kingsley) stumble across an effective weapon against him - a man who has nothing in common with Sanchez except a splitting image. He is Annibal Ramirez (Adrian Quinn), an honorable American naval officer who is ultimately used as bait.
There are two main parts of The Assignment. Part A chronicles an extensive two month training which Ramirez endures, in order to make sure that he thinks and reacts like the man he is impersonating. Shaw and Amos devise a set of physically and mentally challenging tests, including drugging Ramirez with LSD and watching his nightmarish trip. Part B centers on the dangerous environment that Ramirez encounters as he attempts to imitate one of the world's most hunted criminals.
Much to his credit, Adrian Quinn plays both the Jackal and the story's hero with realism and morality. As Ramirez undergoes his training, Quinn transforms his character subtly and brilliantly. The distressing scenarios he encounters reminded me of Michael Douglas' performance in The Game; both actors make their characters paranoia hit viewers hard. In most of his movies, Donald Sutherland is often seen but not noticed. His part in The Assignment is imperative to the story, which is surprising because Sutherland is rarely powerful - but is thoroughly engaging and interesting in this role. Kingsley, as always, is solid.
Fashioned with striking set pieces and gorgeous cinematography, The Assignment is a joy to watch. With a film as smart as this one, we are reassured that good-looking movies are not necessarily dumb ones (for further evidence consult Enemy of the State or Kundun). Solid performances and crisp editing compliment The Assignment's firm direction and beautifully laced screenplay. There are not many films that work on as many levels as this one does, and even fewer that can implement charm alongside terror. Ordinarily this might appear to be a contradiction of terms, but The Assignment has enough substance and energy to pull it off with flair.
Email<EMAIL_ADDRESS>to subscribe to my movie reviews newsletter (unless, of course, you already have) or for any inquiries or feedback
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Rails React Web App Programmer
We are developing several new applications in Rails and React. These projects are in various states of development, from ‘proof-of-concept’ to ‘ready-to-deploy.’ The work also includes leveraging new technologies such as AI and machine learning as service type technologies for custom application implementation. Our work is joined by a common thread of being human-centric and intended to serve our clients with solutions rooted in ethical principles.
Our teams include back-end and front-end developers, UX designers, graphic designers, product managers, QA technicians, and project managers. We work closely and collaboratively and meet regularly with the clients. Our teams are distributed around the world and meet via video conference.
CoLab coworkers bring a wide range of talent and perspectives to our collaboration. We’re looking for people who try to meet complexity with simplicity and can communicate about it with their peers. As a co-op, we embrace both autonomy and mutual aid. You’ll have clear responsibilities in your role, as well as opportunities to contribute in other areas. We have no criteria around particular background or credentials; we care about your proposals, what you produce, and your growth and happiness with our team. Does CoLab sound like a place for you? Read on.
- 5+ years working with Rails, in a service-oriented architecture environment, both classic rails and api only projects
- 3+ years experience managing production code with version control and project management tools
- Strong knowledge of JavaScript, HTML, CSS
- Solid understanding of object-oriented programming
- Identify bottlenecks and bugs, and devise solutions to those
- Experience successfully taking design handoffs from UI/UX concept to production application launch.
- Experience working in Agile Development Methodologies and frameworks such as Scrum
- Maintain code quality, organization, and automatization
- Deliver quality, professional work on a daily basis as a matter of principle.
- Work with distributed teams using online collaboration tools like Slack and Zoom.
- Support project ‘retrospectives’ with insightful feedback.
- Support reviews of peers and help to optimize processes.
- Desire to actively engage in constructive code and design reviews with peers and partners.
- API design structures
- Unit and functional testing challenges
- Creating engaging and responsive interfaces
- Building intuitive data-driven interactions
- Developing efficient and reusable front-end components
- Making data more accessible using natural language technologies
- Analyzing and improving application usability
- A commitment to doing impactful work that benefits the common good.
- A love for simple, elegant solutions.
- Clear communication when setting and managing expectations.
- Commitment to meeting expectations to the best of your ability.
- Show care for yourself, your peers, the planet.
- A flexible mindset when considering how best to work and collaborate together.
- Open to contributing and receiving growth-oriented feedback.
- Empathetic, service-minded, when working directly with clients and peers.
- Ability to communicate in a clear and sensitive manner.
- 2+ years of experience working with React.
- Experience with other custom application frameworks like Django and/or Node.js.
- Experience building client and peer trust and goodwill in an agency role.
- Ability to spot problems before they happen and suggest solutions.
- Experience with technical estimation and roadmap planning.
- Experience with Typescript and component-based development
- Experience with bash scripting and basic Linux server administration.
- Experience with automated user interface testing frameworks.
- Experience with continuous integration and deployments.
- Evaluate features through a human-centric lens.
- Demonstrate good design principles so that app development work has rhythm, balance, and ease of use.
- Ability to apply quantitative reasoning and UX design principles to building applications.
- Demonstrated sense for web design and UX fundamentals, including usability and accessibility best practices.
- Demonstrated ability to make practical design/implementation tradeoff decisions when choosing how to implement feature requests.
CoLab is an international, worker-owned tech collective that builds custom apps and websites for a better world. Our co-op was founded in 2010 to co-create simple, purpose-driven solutions to global problems. We are committed to our clients for the long haul, and we bring our entire team to every engagement. We run a very open, flat, and collaborative organization, and invest in one another as friends and coworkers. Our co-op has a path to becoming a member-ownership, which we hope you’ll consider.
- A path to member-ownership for every worker at CoLab.coop.
- A commitment to work at the intersection of technology and social and environmental justice.
- Welcoming of a diversity of perspectives and experiences. Stronger together.
- Be curious. Listen deeply. Reflect, learn, and grow together.
- Value generosity and transparency.
- Values a familial environment, where our core strength lies not in smarts, but in our togetherness.
- See business relationships, and specifically co-operative economics, as a central pillar of efforts to better our communities.
- Recognize that we are humans before we are of any particular gender, race, class, or religion.
- Understand that our work is collaborative, from the ground up. We support worker agency, listening with empathy, speaking with balance, and cultivating unity.
- Each is equitably valued for their contributions, regardless of their appearance, identity, or beliefs.
- See that working in collaboration with peers as a duo is an inherent strength in most situations.
We are open to receiving applications from wherever you are based including to applicants living outside of the U.S. We prioritize hiring within locales where our current work community resides when possible to allow for in real life meetup opportunities.
Our working hours are mainly +/- 6 hours Eastern time zone. We currently have teammates and clients from California to New York to the EU to the Asia Pacific region.
Hourly, commensurate with experience
Flexible project based work. Contractor agreement determined by skills, capacity, availability and project fit. Opportunity to grow to full employment and member ownership.
Applications are due by January 15, 2020. Anticipated start timing is immediate.
Please email<EMAIL_ADDRESS>with the subject line “Web App Programmer.”
WE CENTER UNDERREPRESENTED PEOPLE AND COMMUNITIES IN OUR WORK. WOMEN, GENDER NON-CONFORMING PEOPLE, INDIGENOUS PEOPLE, AND PEOPLE OF COLOR STRONGLY ENCOURAGED TO APPLY.
- A brief, 2-3 paragraph cover letter about your background, interest in co-operatives and what you hope to bring to this work
- A resume or CV and portfolio or professional links
- Availability to start and other considerations
- 2 questions you have for us
- 2-3 work references
We look forward to talking with you!


The application makes use of Heroku, an open source PaaS, with a Bootstrap framework. The CSS and HTML were customized for simplicity, but maintain the connectivity between the various components (using Flask). All documents are maintained on Github (see link above).
In order for the flask application to work, we needed to provide the (1) model, our Decision Tree Regressor, (2) the fitted StandardScaler and (3) supporting data to the application. These needed to be compiled and used. The specific python packages that were used in code development in the jupyter notebook (on GitHub, see above), are also provided in requirement.txt for the Heroku application to use.
The directory layout includes Templates and Static, with these key files procfile and app.py. The templates directory contains the HTML framework for each site, and the appropriate code to interact with the app.py (which loads data, model artifacts, transforms data using python, predicts, and renders/returns the site as designed.) The static directory contains statics files, images, models, and data used in the application or elsewhere. Procfile is a supporting file instructing Heroku which application (here app.py) to run. All are uploaded to Heroku in which this application is hosted.
The general steps for the Data Science Workflow is listed below, and was iterative in nature, as more was learned throughout the process. In GitHub (see link above), the jupyter notebook shows all code, object creation, and fully discusses this project.
The final model chosen needed to generalize well to unseen data. Even though the earlier models performed well on the training data (accuracy ~95%), the test accuracy was much lower (50%). This indicates overfitting and poor generalization, i.e. our new prediction in the application would not be that great.
So, the final model chosen was a Decision Tree Regression, trained using cross-validation and grid searching of hyperparameters. This resulted in a lower training metric accuracy of 81.5% but a much closer (and higher) test accuracy of 77.2%. The closeness, ~5% indicates that the model is generalizing well to unseen data. As a result, the best estimator was used, saved, and deployed. Further improvement and next steps are discussed in the jupyter notebook.


About Me
wisiwig what you see is what you
get.
adkme and the fox run away from the car
and the bird gor eaten by the cat.
the end.


Model Projection
In Relational algebra, projection means collecting a subset of columns for use in operations, i.e. a projection is the list of columns selected.
Typically projection is used to lookup data from one model to the master data model, and it's defined as nested class derived from Projection:
public class Product : ...
{
...
public class Lookup : Projection
{
static Lookup()
{
Register((Lookup _) => _.Name, _Name);
Register((Lookup _) => _.ProductNumber, _ProductNumber);
}
public _String Name { get; private set; }
public _String ProductNumber { get; private set; }
}
...
}
The defined projection class can be used for other model:
public class SalesOrderInfoDetail : ...
{
static SalesOrderInfoDetail()
{
RegisterProjection((SalesOrderInfoDetail _) => _.Product);
}
public Product.Lookup Product { get; private set; }
}
You can add projection by clicking the left top drop down button of Model Visualizer tool window, then click 'Add Projection...':
The following dialog will be displayed:
Fill the dialog form and click "OK", code of a projection will be generated automatically.


nips nips2011 nips2011-272 nips2011-272-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Abstract: This paper addresses the problem of minimizing a convex, Lipschitz function f over a convex, compact set X under a stochastic bandit feedback model. In this model, the algorithm is allowed to observe noisy realizations of the function value f (x) at any query point x ∈ X . We demonstrate √ a generalization of the ellipsoid algorithm that √ incurs O(poly(d) T ) regret. Since any algorithm has regret at least Ω( T ) on this problem, our algorithm is optimal in terms of the scaling with T . 1
[1] A. Agarwal, O. Dekel, and L. Xiao. Optimal algorithms for online convex optimization with multi-point bandit feedback. In COLT, 2010.
[2] A. Agarwal, D. Foster, D. Hsu, S. Kakade, and A. Rakhlin. Stochastic convex optimization with bandit feedback. URL http://arxiv.org/abs/1107.1744, 2011.
[3] R. Agrawal. The continuum-armed bandit problem. SIAM journal on control and optimization, 33:1926, 1995.
[4] P. Auer, R. Ortner, and C. Szepesv´ri. Improved rates for the stochastic continuuma armed bandit problem. Learning Theory, pages 454–468, 2007.
[5] K. Ball. An elementary introduction to modern convex geometry. In Flavors of Geometry, number 31 in Publications of the Mathematical Sciences Research Institute, pages 1–55. 1997.
[6] D. Bertsimas and S. Vempala. Solving convex programs by random walks. Journal of the ACM, 51(4):540–556, 2004.
[7] S. Bubeck, R. Munos, G. Stolz, and C. Szepesv´ri. X -armed bandits. Journal of a Machine Learning Research, 12:1655–1695, 2011.
[8] E.W. Cope. Regret and convergence bounds for a class of continuum-armed bandit problems. Automatic Control, IEEE Transactions on, 54(6):1243–1253, 2009.
[9] V. Dani, T.P. Hayes, and S.M. Kakade. Stochastic linear optimization under bandit feedback. In Proceedings of the 21st Annual Conference on Learning Theory (COLT), 2008.
[10] A. D. Flaxman, A. T. Kalai, and B. H. Mcmahan. Online convex optimization in the bandit setting: gradient descent without a gradient. In Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, pages 385–394, 2005.
[11] Donald Goldfarb and Michael J. Todd. Modiﬁcations and implementation of the ellipsoid algorithm for linear programming. Mathematical Programming, 23:1–19, 1982.
[12] R. Kleinberg. Nearly tight bounds for the continuum-armed bandit problem. Advances in Neural Information Processing Systems, 18, 2005.
[13] R. Kleinberg, A. Slivkins, and E. Upfal. Multi-armed bandits in metric spaces. In Proceedings of the 40th annual ACM symposium on Theory of computing, pages 681– 690. ACM, 2008.
[14] A. Nemirovski and D. Yudin. Problem Complexity and Method Eﬃciency in Optimization. Wiley, New York, 1983.
[15] Y. Nesterov. Random gradient-free minimization of convex functions. Technical Report 2011/1, CORE DP, 2011.
[16] N. Srinivas, A. Krause, S.M. Kakade, and M. Seeger. Gaussian process optimization in the bandit setting: No regret and experimental design. Arxiv preprint arXiv:0912.3995, 2009.
[17] J. Y. Yu and S. Mannor. Unimodal bandits. In ICML, 2011. 9


Welcome to my portfolio! My name is John Vance, and i'm Game Designer/Developer with a wealth of experience in Systems Design, Combat Design, Gameplay Programming, and Software Engineering. With a passion for creating engaging and immersive player focused experiences, I am is determined to break into the industry and make a name for myself. I love collaberating with others to create amazing experiences through games and with my additional background in audio and asset production, I can communicate with more artistic disciplines well. Whether working independently or as part of a team, I am committed to bringing my unique perspective and expertise to every project I undertake.


Test page for most matching 2 offers
Publisher (Required)
What
Where (or Lat & Lon)
tag:
Latitude:
Longitude:
Expires Before:
Customer Has Budget:
Radius (Required if Lat & Lon provided)
Client's IP(Required)