Group by: split-apply-combine#By “group by” we are referring to a process involving one or more of the following steps: Show
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:
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 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. Splitting an object into groups#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:
Collectively we refer to the grouping objects as the keys. For example, consider the following Note A string passed to 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 In [8]: grouped = df.groupby("A") In [9]: grouped = df.groupby(["A", "B"]) If we also have a MultiIndex on 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 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. Note 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. GroupBy sorting#By default the group keys are sorted during the 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 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 New in version 1.1.0. GroupBy dropna#By default In [27]: df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]] In [28]: df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c"]) In [29]: df_dropna Out[29]: a b c 0 1 2.0 3 1 1 NaN 4 2 2 1.0 3 3 1 2.0 2 # Default ``dropna`` is set to True, which will exclude NaNs in keys In [30]: df_dropna.groupby(by=["b"], dropna=True).sum() Out[30]: a c b 1.0 2 3 2.0 2 5 # In order to allow NaN in keys, set ``dropna`` to False In [31]: df_dropna.groupby(by=["b"], dropna=False).sum() Out[31]: a c b 1.0 2 3 2.0 2 5 NaN 1 4 The default setting of GroupBy object attributes#The In [32]: df.groupby("A").groups Out[32]: {'bar': [1, 3, 5], 'foo': [0, 2, 4, 6, 7]} In [33]: df.groupby(get_letter_type, axis=1).groups Out[33]: {'consonant': ['B', 'C', 'D'], 'vowel': ['A']} Calling the standard Python In [34]: grouped = df.groupby(["A", "B"]) In [35]: grouped.groups Out[35]: {('bar', 'one'): [1], ('bar', 'three'): [3], ('bar', 'two'): [5], ('foo', 'one'): [0, 6], ('foo', 'three'): [7], ('foo', 'two'): [2, 4]} In [36]: len(grouped) Out[36]: 6
In [37]: df Out[37]: 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 [38]: gb = df.groupby("gender") In [39]: 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 GroupBy with MultiIndex#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 In [40]: arrays = [ ....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ....: ["one", "two", "one", "two", "one", "two", "one", "two"], ....: ] ....: In [41]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"]) In [42]: s = pd.Series(np.random.randn(8), index=index) In [43]: s Out[43]: 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 In [44]: grouped = s.groupby(level=0) In [45]: grouped.sum() Out[45]: 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 [46]: s.groupby(level="second").sum() Out[46]: second one 0.980950 two 1.991575 dtype: float64 Grouping with multiple levels is supported. In [47]: s Out[47]: 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 [48]: s.groupby(level=["first", "second"]).sum() Out[48]: 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 [49]: s.groupby(["first", "second"]).sum() Out[49]: first second bar doo -1.220674 baz bee -0.608004 foo bop 1.023898 qux bop 0.000895 dtype: float64 More on the Grouping DataFrame with Index levels and columns#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 In [50]: arrays = [ ....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ....: ["one", "two", "one", "two", "one", "two", "one", "two"], ....: ] ....: In [51]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"]) In [52]: df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 3, 3], "B": np.arange(8)}, index=index) In [53]: df Out[53]: 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 In [54]: df.groupby([pd.Grouper(level=1), "A"]).sum() Out[54]: 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 [55]: df.groupby([pd.Grouper(level="second"), "A"]).sum() Out[55]: 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 In [56]: df.groupby(["second", "A"]).sum() Out[56]: B second A one 1 2 2 4 3 6 two 1 4 2 5 3 7 DataFrame column selection in GroupBy#Once you have created the GroupBy object from a DataFrame, you might want to do
something different for each of the columns. Thus, using In [57]: 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 [58]: df Out[58]: 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 [59]: grouped = df.groupby(["A"]) In [60]: grouped_C = grouped["C"] In [61]: grouped_D = grouped["D"] This is mainly syntactic sugar for the alternative and much more verbose: In [62]: df["C"].groupby(df["A"]) Out[62]: <pandas.core.groupby.generic.SeriesGroupBy object at 0x7f12a6b706d0> Additionally this method avoids recomputing the internal grouping information derived from the passed key. Iterating through groups#With the GroupBy object in hand, iterating through the grouped data is very natural and functions similarly to In [63]: grouped = df.groupby('A') In [64]: 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 [65]: 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. Selecting a group#A single group can be selected using In [66]: grouped.get_group("bar") Out[66]: 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 [67]: df.groupby(["A", "B"]).get_group(("bar", "one")) Out[67]: A B C D 1 bar one 0.254161 1.511763 Aggregation#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 API, and resample API. An obvious one is aggregation via the
In [68]: grouped = df.groupby("A") In [69]: grouped[["C", "D"]].aggregate(np.sum) Out[69]: C D A bar 0.392940 1.732707 foo -1.796421 2.824590 In [70]: grouped = df.groupby(["A", "B"]) In [71]: grouped.aggregate(np.sum) Out[71]: 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 In [72]: grouped = df.groupby(["A", "B"], as_index=False) In [73]: grouped.aggregate(np.sum) Out[73]: 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 [74]: df.groupby("A", as_index=False)[["C", "D"]].sum() Out[74]: A C D 0 bar 0.392940 1.732707 1 foo -1.796421 2.824590 Note that you could use the In [75]: df.groupby(["A", "B"]).sum().reset_index() Out[75]: 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
In [76]: grouped.size() Out[76]: A B size 0 bar one 1 1 bar three 1 2 bar two 1 3 foo one 2 4 foo three 1 5 foo two 2 In [77]: grouped.describe() Out[77]: C ... D count mean std min ... 25% 50% 75% max 0 1.0 0.254161 NaN 0.254161 ... 1.511763 1.511763 1.511763 1.511763 1 1.0 0.215897 NaN 0.215897 ... -0.990582 -0.990582 -0.990582 -0.990582 2 1.0 -0.077118 NaN -0.077118 ... 1.211526 1.211526 1.211526 1.211526 3 2.0 -0.491888 0.117887 -0.575247 ... 0.537905 0.807291 1.076676 1.346061 4 1.0 -0.862495 NaN -0.862495 ... 0.024580 0.024580 0.024580 0.024580 5 2.0 0.024925 1.652692 -1.143704 ... 0.075531 0.592714 1.109898 1.627081 [6 rows x 16 columns] Another aggregation example is to compute the number of unique values of each group. This is similar to the In [78]: ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]] In [79]: df4 = pd.DataFrame(ll, columns=["A", "B"]) In [80]: df4 Out[80]: A B 0 foo 1 1 foo 2 2 foo 2 3 bar 1 4 bar 1 In [81]: df4.groupby("A")["B"].nunique() Out[81]: A bar 1 foo 2 Name: B, dtype: int64 Note Aggregation functions will not return the groups that you are aggregating over if they are
named columns, when Passing Aggregating functions are the ones that reduce the dimension of the returned objects. Some common aggregating functions are tabulated below:
The aggregating functions above will exclude NA values. Any function which reduces a Applying multiple functions at once#With grouped In [82]: grouped = df.groupby("A") In [83]: grouped["C"].agg([np.sum, np.mean, np.std]) Out[83]: sum mean std A bar 0.392940 0.130980 0.181231 foo -1.796421 -0.359284 0.912265 On a grouped In [84]: grouped[["C", "D"]].agg([np.sum, np.mean, np.std]) Out[84]: 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 In [85]: ( ....: grouped["C"] ....: .agg([np.sum, np.mean, np.std]) ....: .rename(columns={"sum": "foo", "mean": "bar", "std": "baz"}) ....: ) ....: Out[85]: foo bar baz A bar 0.392940 0.130980 0.181231 foo -1.796421 -0.359284 0.912265 For a grouped In [86]: ( ....: grouped[["C", "D"]].agg([np.sum, np.mean, np.std]).rename( ....: columns={"sum": "foo", "mean": "bar", "std": "baz"} ....: ) ....: ) ....: Out[86]: 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
Note 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 [87]: grouped["C"].agg(["sum", "sum"]) Out[87]: 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 In [88]: grouped["C"].agg([lambda x: x.max() - x.min(), lambda x: x.median() - x.mean()]) Out[88]: <lambda_0> <lambda_1> A bar 0.331279 0.084917 foo 2.337259 -0.215962 Named aggregation#New in version 0.25.0. To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in
In [89]: 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 [90]: animals Out[90]: 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 [91]: 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), ....: ) ....: Out[91]: min_height max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75
In [92]: animals.groupby("kind").agg( ....: min_height=("height", "min"), ....: max_height=("height", "max"), ....: average_weight=("weight", np.mean), ....: ) ....: Out[92]: 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 [93]: animals.groupby("kind").agg( ....: **{ ....: "total weight": pd.NamedAgg(column="weight", aggfunc=sum) ....: } ....: ) ....: Out[93]: total weight kind cat 17.8 dog 205.5 Additional keyword arguments are not passed through to the aggregation functions. Only pairs of Note For Python 3.5 and earlier, the order of 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 [94]: animals.groupby("kind").height.agg( ....: min_height="min", ....: max_height="max", ....: ) ....: Out[94]: min_height max_height kind cat 9.1 9.5 dog 6.0 34.0 Applying different functions to DataFrame columns#By passing a dict to In [95]: grouped.agg({"C": np.sum, "D": lambda x: np.std(x, ddof=1)}) Out[95]: 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 [96]: grouped.agg({"C": "sum", "D": "std"}) Out[96]: C D A bar 0.392940 1.366330 foo -1.796421 0.884785 Cython-optimized aggregation functions#Some common aggregations, currently only In [97]: df.groupby("A")[["C", "D"]].sum() Out[97]: C D A bar 0.392940 1.732707 foo -1.796421 2.824590 In [98]: df.groupby(["A", "B"]).mean() Out[98]: 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 Aggregations with User-Defined Functions#Users can also provide their own functions for custom aggregations. When aggregating with a User-Defined Function
(UDF), the UDF should not mutate the provided In [99]: animals.groupby("kind")[["height"]].agg(lambda x: set(x)) Out[99]: height kind cat {9.1, 9.5} dog {34.0, 6.0} The resulting dtype will reflect that of the aggregating function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as In [100]: animals.groupby("kind")[["height"]].agg(lambda x: x.astype(int).sum()) Out[100]: height kind cat 18 dog 40 Transformation#The
Deprecated since version 1.5.0: When using Similar to
Aggregations with User-Defined Functions, the resulting dtype will reflect that of the transformation function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as Suppose we wished to standardize the data within each group: In [101]: index = pd.date_range("10/1/1999", periods=1100) In [102]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index) In [103]: ts = ts.rolling(window=100, min_periods=100).mean().dropna() In [104]: ts.head() Out[104]: 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 [105]: ts.tail() Out[105]: 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 [106]: 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 [107]: grouped = ts.groupby(lambda x: x.year) In [108]: grouped.mean() Out[108]: 2000 0.442441 2001 0.526246 2002 0.459365 dtype: float64 In [109]: grouped.std() Out[109]: 2000 0.131752 2001 0.210945 2002 0.128753 dtype: float64 # Transformed Data In [110]: grouped_trans = transformed.groupby(lambda x: x.year) In [111]: grouped_trans.mean() Out[111]: 2000 -4.870756e-16 2001 -1.545187e-16 2002 4.136282e-16 dtype: float64 In [112]: grouped_trans.std() Out[112]: 2000 1.0 2001 1.0 2002 1.0 dtype: float64 We can also visually compare the original and transformed data sets. In [113]: compare = pd.DataFrame({"Original": ts, "Transformed": transformed}) In [114]: compare.plot() Out[114]: <AxesSubplot: > Transformation functions that have lower dimension outputs are broadcast to match the shape of the input array. In [115]: ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min()) Out[115]: 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 [116]: max_ts = ts.groupby(lambda x: x.year).transform("max") In [117]: min_ts = ts.groupby(lambda x: x.year).transform("min") In [118]: max_ts - min_ts Out[118]: 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 [119]: data_df Out[119]: 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 [120]: countries = np.array(["US", "UK", "GR", "JP"]) In [121]: key = countries[np.random.randint(0, 4, 1000)] In [122]: grouped = data_df.groupby(key) # Non-NA count in each group In [123]: grouped.count() Out[123]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217 In [124]: 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 [125]: grouped_trans = transformed.groupby(key) In [126]: grouped.mean() # original group means Out[126]: 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 [127]: grouped_trans.mean() # transformation did not change group means Out[127]: 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 [128]: grouped.count() # original has some missing data points Out[128]: A B C GR 209 217 189 JP 240 255 217 UK 216 231 193 US 239 250 217 In [129]: grouped_trans.count() # counts after transformation Out[129]: A B C GR 228 228 228 JP 267 267 267 UK 247 247 247 US 258 258 258 In [130]: grouped_trans.size() # Verify non-NA count equals group size Out[130]: GR 228 JP 267 UK 247 US 258 dtype: int64 Note 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 For example: In [131]: grouped.ffill() Out[131]: 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] Window and resample operations#It is possible to use The example below will apply
the In [132]: df_re = pd.DataFrame({"A": [1] * 10 + [5] * 10, "B": np.arange(20)}) In [133]: df_re Out[133]: 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 [134]: df_re.groupby("A").rolling(4).B.mean() Out[134]: 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 In [135]: df_re.groupby("A").expanding().sum() Out[135]: B A 1 0 0.0 1 1.0 2 3.0 3 6.0 4 10.0 ... ... 5 15 75.0 16 91.0 17 108.0 18 126.0 19 145.0 [20 rows x 1 columns] Suppose you want to use the In [136]: 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 [137]: df_re Out[137]: group val date 2016-01-03 1 5 2016-01-10 1 6 2016-01-17 2 7 2016-01-24 2 8 In [138]: df_re.groupby("group").resample("1D").ffill() Out[138]: 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] Filtration#The In [139]: sf = pd.Series([1, 1, 2, 3, 3, 3]) In [140]: sf.groupby(sf).filter(lambda x: x.sum() > 2) Out[140]: 3 3 4 3 5 3 dtype: int64 The argument of Another useful operation is filtering out elements that belong to groups with only a couple members. In [141]: dff = pd.DataFrame({"A": np.arange(8), "B": list("aabbbbcc")}) In [142]: dff.groupby("B").filter(lambda x: len(x) > 2) Out[142]: 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 [143]: dff.groupby("B").filter(lambda x: len(x) > 2, dropna=False) Out[143]: 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 [144]: dff["C"] = np.arange(8) In [145]: dff.groupby("B").filter(lambda x: len(x["C"]) > 2) Out[145]: A B C 2 2 b 2 3 3 b 3 4 4 b 4 5 5 b 5 Note 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 For example: In [146]: dff.groupby("B").head(2) Out[146]: 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 Dispatching to instance methods#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 [147]: grouped = df.groupby("A") In [148]: grouped.agg(lambda x: x.std()) Out[148]: 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 [149]: grouped.std() Out[149]: 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 In [150]: tsdf = pd.DataFrame( .....: np.random.randn(1000, 3), .....: index=pd.date_range("1/1/2000", periods=1000), .....: columns=["A", "B", "C"], .....: ) .....: In [151]: tsdf.iloc[::2] = np.nan In [152]: grouped = tsdf.groupby(lambda x: x.year) In [153]: grouped.fillna(method="pad") Out[153]: 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 In [154]: s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3]) In [155]: g = pd.Series(list("abababab")) In [156]: gb = s.groupby(g) In [157]: gb.nlargest(3) Out[157]: a 4 19.0 0 9.0 2 7.0 b 1 8.0 3 5.0 7 3.3 dtype: float64 In [158]: gb.nsmallest(3) Out[158]: a 6 4.2 2 7.0 0 9.0 b 5 1.0 7 3.3 3 5.0 dtype: float64 Flexible apply#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 Note
In [159]: df Out[159]: 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 [160]: grouped = df.groupby("A") # could also just call .describe() In [161]: grouped["C"].apply(lambda x: x.describe()) Out[161]: 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 [162]: grouped = df.groupby('A')['C'] In [163]: def f(group): .....: return pd.DataFrame({'original': group, .....: 'demeaned': group - group.mean()}) .....:
In [164]: def f(x): .....: return pd.Series([x, x ** 2], index=["x", "x^2"]) .....: In [165]: s = pd.Series(np.random.rand(5)) In [166]: s Out[166]: 0 0.321438 1 0.493496 2 0.139505 3 0.910103 4 0.194158 dtype: float64 In [167]: s.apply(f) Out[167]: 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 Control grouped column(s) placement with group_keys#Note If Changed in version 1.5.0. To control whether the grouped column(s) are included in the indices, you can use the argument In [168]: df.groupby("A", group_keys=True).apply(lambda x: x) Out[168]: A B C D A bar 1 bar one 0.254161 1.511763 3 bar three 0.215897 -0.990582 5 bar two -0.077118 1.211526 foo 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 with In [169]: df.groupby("A", group_keys=False).apply(lambda x: x) Out[169]: 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 Similar to Aggregations with User-Defined
Functions, the resulting dtype will reflect that of the apply function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as Numba Accelerated Routines#New in version 1.1. If Numba is installed as an optional dependency, the The function signature must start with Warning When using Other useful features#Automatic exclusion of “nuisance” columns#Again consider the example DataFrame we’ve been looking at: In [170]: df Out[170]: 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 In [171]: df.groupby("A").std(numeric_only=True) Out[171]: C D A bar 0.181231 1.366330 foo 0.912265 0.884785 Note that Note Any object column, also if it contains numerical values such as If you do wish to include decimal or object columns in an aggregation with other non-nuisance data types, you must do so explicitly. Warning The automatic dropping of nuisance columns has been deprecated and will be removed in a future
version of pandas. If columns are included that cannot be operated on, pandas will instead raise an error. In order to avoid this, either select the columns you wish to operate on or specify In [172]: from decimal import Decimal In [173]: 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 [174]: df_dec.groupby(["id"])[["dec_column"]].sum() Out[174]: dec_column id 1 0.75 2 0.55 # ...but cannot be combined with standard data types or they will be excluded In [175]: df_dec.groupby(["id"])[["int_column", "dec_column"]].sum() Out[175]: int_column id 1 4 2 6 # Use .agg function to aggregate over standard and "nuisance" data types # at the same time In [176]: df_dec.groupby(["id"]).agg({"int_column": "sum", "dec_column": "sum"}) Out[176]: int_column dec_column id 1 4 0.75 2 6 0.55 Handling of (un)observed Categorical values#When using a Show all values: In [177]: pd.Series([1, 1, 1]).groupby( .....: pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=False .....: ).count() .....: Out[177]: a 3 b 0 dtype: int64 Show only the observed values: In [178]: pd.Series([1, 1, 1]).groupby( .....: pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=True .....: ).count() .....: Out[178]: a 3 dtype: int64 The returned dtype of the grouped will always include all of the categories that were grouped. In [179]: s = ( .....: pd.Series([1, 1, 1]) .....: .groupby(pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=False) .....: .count() .....: ) .....: In [180]: s.index.dtype Out[180]: CategoricalDtype(categories=['a', 'b'], ordered=False) NA and NaT group handling#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). Grouping with ordered factors#Categorical variables represented as instance of pandas’s In [181]: data = pd.Series(np.random.randn(100)) In [182]: factor = pd.qcut(data, [0, 0.25, 0.5, 0.75, 1.0]) In [183]: data.groupby(factor).mean() Out[183]: (-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 Grouping with a grouper specification#You may need to
specify a bit more data to properly group. You can use the In [184]: import datetime In [185]: 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 [186]: df Out[186]: 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 [187]: df.groupby([pd.Grouper(freq="1M", key="Date"), "Buyer"])[["Quantity"]].sum() Out[187]: 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 [188]: df = df.set_index("Date") In [189]: df["Date"] = df.index + pd.offsets.MonthEnd(2) In [190]: df.groupby([pd.Grouper(freq="6M", key="Date"), "Buyer"])[["Quantity"]].sum() Out[190]: Quantity Date Buyer 2013-02-28 Carl 1 Mark 3 2014-02-28 Carl 9 Joe 18 In [191]: df.groupby([pd.Grouper(freq="6M", level="Date"), "Buyer"])[["Quantity"]].sum() Out[191]: Quantity Date Buyer 2013-01-31 Carl 1 Mark 3 2014-01-31 Carl 9 Joe 18 Taking the first rows of each group#Just like for a DataFrame or Series you can call head and tail on a groupby: In [192]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"]) In [193]: df Out[193]: A B 0 1 2 1 1 4 2 5 6 In [194]: g = df.groupby("A") In [195]: g.head(1) Out[195]: A B 0 1 2 2 5 6 In [196]: g.tail(1) Out[196]: A B 1 1 4 2 5 6 This shows the first or last n rows from each group. Taking the nth row of each group#To select from a DataFrame or Series the nth item, use In [197]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"]) In [198]: g = df.groupby("A") In [199]: g.nth(0) Out[199]: B A 1 NaN 5 6.0 In [200]: g.nth(-1) Out[200]: B A 1 4.0 5 6.0 In [201]: g.nth(1) Out[201]: B A 1 4.0 If you want to select the nth not-null item, use the # nth(0) is the same as g.first() In [202]: g.nth(0, dropna="any") Out[202]: B A 1 4.0 5 6.0 In [203]: g.first() Out[203]: B A 1 4.0 5 6.0 # nth(-1) is the same as g.last() In [204]: g.nth(-1, dropna="any") # NaNs denote group exhausted when using dropna Out[204]: B A 1 4.0 5 6.0 In [205]: g.last() Out[205]: B A 1 4.0 5 6.0 In [206]: g.B.nth(0, dropna="all") Out[206]: A 1 4.0 5 6.0 Name: B, dtype: float64 As with other methods, passing In [207]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"]) In [208]: g = df.groupby("A", as_index=False) In [209]: g.nth(0) Out[209]: A B 0 1 NaN 2 5 6.0 In [210]: g.nth(-1) Out[210]: 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 [211]: business_dates = pd.date_range(start="4/1/2014", end="6/30/2014", freq="B") In [212]: df = pd.DataFrame(1, index=business_dates, columns=["a", "b"]) # get the first, 4th, and last date index for each month In [213]: df.groupby([df.index.year, df.index.month]).nth([0, 3, -1]) Out[213]: 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 Enumerate group items#To see the order in which each row appears within its group, use the In [214]: dfg = pd.DataFrame(list("aaabba"), columns=["A"]) In [215]: dfg Out[215]: A 0 a 1 a 2 a 3 b 4 b 5 a In [216]: dfg.groupby("A").cumcount() Out[216]: 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 In [217]: dfg.groupby("A").cumcount(ascending=False) Out[217]: 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 Enumerate groups#To see the ordering of the groups (as opposed to the order of rows within a group given by 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 [218]: dfg = pd.DataFrame(list("aaabba"), columns=["A"]) In [219]: dfg Out[219]: A 0 a 1 a 2 a 3 b 4 b 5 a In [220]: dfg.groupby("A").ngroup() Out[220]: 0 0 1 0 2 0 3 1 4 1 5 0 dtype: int64 In [221]: dfg.groupby("A").ngroup(ascending=False) Out[221]: 0 1 1 1 2 1 3 0 4 0 5 1 dtype: int64 Plotting#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 [222]: np.random.seed(1234) In [223]: df = pd.DataFrame(np.random.randn(50, 2)) In [224]: df["g"] = np.random.choice(["A", "B"], size=50) In [225]: df.loc[df["g"] == "B", 1] += 3 We can easily visualize this with a boxplot: In [226]: df.groupby("g").boxplot() Out[226]: A AxesSubplot(0.1,0.15;0.363636x0.75) B AxesSubplot(0.536364,0.15;0.363636x0.75) dtype: object The result of calling
Warning For historical reasons, Piping function calls#Similar to the functionality provided by Combining 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 [227]: n = 1000 In [228]: 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 [229]: df.head(2) Out[229]: 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 [230]: ( .....: df.groupby(["Store", "Product"]) .....: .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum()) .....: .unstack() .....: .round(2) .....: ) .....: Out[230]: 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 [231]: def mean(groupby): .....: return groupby.mean() .....: In [232]: df.groupby(["Store", "Product"]).pipe(mean) Out[232]: 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 Examples#Regrouping by factor#Regroup columns of a DataFrame according to their sum, and sum the aggregated ones. In [233]: df = pd.DataFrame({"a": [1, 0, 0], "b": [0, 1, 0], "c": [1, 0, 0], "d": [2, 3, 4]}) In [234]: df Out[234]: a b c d 0 1 0 1 2 1 0 1 0 3 2 0 0 0 4 In [235]: df.groupby(df.sum(), axis=1).sum() Out[235]: 1 9 0 2 2 1 1 3 2 0 4 Multi-column factorization#By using In [236]: dfg = pd.DataFrame({"A": [1, 1, 2, 3, 2], "B": list("aaaba")}) In [237]: dfg Out[237]: A B 0 1 a 1 1 a 2 2 a 3 3 b 4 2 a In [238]: dfg.groupby(["A", "B"]).ngroup() Out[238]: 0 0 1 0 2 1 3 2 4 1 dtype: int64 In [239]: dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup() Out[239]: 0 0 1 0 2 1 3 3 4 2 dtype: int64 Groupby by indexer to ‘resample’ data#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. Note 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 [240]: df = pd.DataFrame(np.random.randn(10, 2)) In [241]: df Out[241]: 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 [242]: df.index // 5 Out[242]: Int64Index([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype='int64') In [243]: df.groupby(df.index // 5).std() Out[243]: 0 1 0 0.823647 1.312912 1 0.760109 0.942941 Returning a Series to propagate names#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 [244]: 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 [245]: def compute_metrics(x): .....: result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()} .....: return pd.Series(result, name="metrics") .....: In [246]: result = df.groupby("a").apply(compute_metrics) In [247]: result Out[247]: metrics b_sum c_mean a 0 2.0 0.5 1 2.0 0.5 2 2.0 0.5 In [248]: result.stack() Out[248]: 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 Which options can you select in the custom Autofilter dialog box if you want to specify two criteria?To filter for two criteria:. From the first dropdown list, select an option, such as "begins with".. In the text box, type a value, then Click OK.. Select "And" or "Or" as the operator.. From the first dropdown list, select an option, such as "ends with".. In the text box, type a value, then Click OK.. What is the most commonly used tab in the Filter dialog box?The Home tab contains the most commonly used commands and options from the Format, Design, Filter, and Report groups. It is shown in the following image.
When would you use the then by option at the Sort dialog box?Mid-term Concepts; Intro to Spread Sheets. When you click a n ____ the computer switches to the file or portion of the file that it references?Excel Module 6. |