pandas
python
import numpy as np np.random.seed(123456) np.set_printoptions(precision=4, suppress=True) import pandas as pd pd.options.display.max_rows = 15 import matplotlib matplotlib.style.use('ggplot') import matplotlib.pyplot as plt plt.close('all') from collections import OrderedDict
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
Of these, the split step is the most straightforward. In fact, in many situations you may wish to split the data set into groups and do something with those groups yourself. In the apply step, we might wish to one of the following:
Aggregation: computing a summary statistic (or statistics) about each group. Some examples:
- Compute group sums or means
- Compute group sizes / counts
Transformation: perform some group-specific computations and return a like-indexed. Some examples:
- Standardizing data (zscore) within 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:
- Discarding data that belongs to groups with only a few members
- Filtering 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:
SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2We 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<cookbook.grouping> 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 do the following:
# default is axis=0
>>> grouped = obj.groupby(key)
>>> grouped = obj.groupby(key, axis=1)
>>> grouped = obj.groupby([key1, key2])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 namemapping- For DataFrame objects, a string indicating a column to be used to group. Of course
df.groupby('A')is just syntactic sugar fordf.groupby(df['A']), but it makes life simpler- A list of any of the above things
Collectively we refer to the grouping objects as the keys. For example, consider the following DataFrame:
python
- 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)})
df
We could naturally group by either the A or B columns or both:
python
grouped = df.groupby('A') grouped = df.groupby(['A', 'B'])
These will split the DataFrame on its index (rows). We could also split by the columns:
- In [4]: def get_letter_type(letter):
...: if letter.lower() in 'aeiou': ...: return 'vowel' ...: else: ...: return 'consonant' ...:
In [5]: grouped = df.groupby(get_letter_type, axis=1)
Starting with 0.8, pandas Index objects now 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:
python
lst = [1, 2, 3, 1, 2, 3]
s = pd.Series([1, 2, 3, 10, 20, 30], lst)
grouped = s.groupby(level=0)
grouped.first()
grouped.last()
grouped.sum()
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.
By default the group keys are sorted during the groupby operation. You may however pass sort=False for potential speedups:
python
df2 = pd.DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]}) df2.groupby(['X']).sum() df2.groupby(['X'], sort=False).sum()
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:
python
df3 = pd.DataFrame({'X' : ['A', 'B', 'A', 'B'], 'Y' : [1, 4, 3, 2]}) df3.groupby(['X']).get_group('A')
df3.groupby(['X']).get_group('B')
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:
python
df.groupby('A').groups df.groupby(get_letter_type, axis=1).groups
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:
python
grouped = df.groupby(['A', 'B']) grouped.groups len(grouped)
GroupBy will tab complete column names (and other attributes)
python
n = 10 weight = np.random.normal(166, 20, size=n) height = np.random.normal(60, 10, size=n) time = pd.date_range('1/1/2000', periods=n) gender = np.random.choice(['male', 'female'], size=n) df = pd.DataFrame({'height': height, 'weight': weight, 'gender': gender}, index=time)
python
df gb = df.groupby('gender')
@verbatim In [1]: gb.<TAB> 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
python
- 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)})
With hierarchically-indexed data <advanced.hierarchical>, it's quite natural to group by one of the levels of the hierarchy.
Let's create a Series with a two-level MultiIndex.
python
- arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second']) s = pd.Series(np.random.randn(8), index=index) s
We can then group by one of the levels in s.
python
grouped = s.groupby(level=0) grouped.sum()
If the MultiIndex has names specified, these can be passed instead of the level number:
python
s.groupby(level='second').sum()
The aggregation functions such as sum will take the level parameter directly. Additionally, the resulting index will be named according to the chosen level:
python
s.sum(level='second')
Also as of v0.6, grouping with multiple levels is supported.
python
- arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['doo', 'doo', 'bee', 'bee', 'bop', 'bop', 'bop', 'bop'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays)) index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second', 'third']) s = pd.Series(np.random.randn(8), index=index)
python
s s.groupby(level=['first', 'second']).sum()
More on the sum function and aggregation later.
Once you have created the GroupBy object from a DataFrame, for example, 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:
python
grouped = df.groupby(['A']) grouped_C = grouped['C'] grouped_D = grouped['D']
This is mainly syntactic sugar for the alternative and much more verbose:
python
df['C'].groupby(df['A'])
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:
In [4]: grouped = df.groupby('A')
- In [5]: for name, group in grouped:
...: print(name) ...: print(group) ...:
In the case of grouping by multiple keys, the group name will be a tuple:
- In [5]: for name, group in df.groupby(['A', 'B']):
...: print(name) ...: print(group) ...:
It's standard Python-fu but remember you can unpack the tuple in the for loop statement if you wish: for (k1, k2), group in grouped:.
A single group can be selected using GroupBy.get_group():
python
grouped.get_group('bar')
Or for an object grouped on multiple columns:
python
df.groupby(['A', 'B']).get_group(('bar', 'one'))
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 <basics.aggregate>, window functions <stats.aggregate>, and resample API <timeseries.aggregate>.
An obvious one is aggregation via the aggregate or equivalently agg method:
python
grouped = df.groupby('A') grouped.aggregate(np.sum)
grouped = df.groupby(['A', 'B']) grouped.aggregate(np.sum)
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 <advanced.hierarchical> by default, though this can be changed by using the as_index option:
python
grouped = df.groupby(['A', 'B'], as_index=False) grouped.aggregate(np.sum)
df.groupby('A', as_index=False).sum()
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:
python
df.groupby(['A', 'B']).sum().reset_index()
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.
python
grouped.size()
python
grouped.describe()
Note
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.
Passing as_index=False will return the groups that you are aggregating over, if they are named columns.
Aggregating functions are ones that reduce the dimension of the returned objects, for example: mean, sum, size, count, std, var, sem, describe, first, last, nth, min, max. This is what happens when you do for example DataFrame.sum() and get back a Series.
nth can act as a reducer or a filter, see here <groupby.nth>
With grouped Series you can also pass a list or dict of functions to do aggregation with, outputting a DataFrame:
python
grouped = df.groupby('A') grouped['C'].agg([np.sum, np.mean, np.std])
If a dict is passed, the keys will be used to name the columns. Otherwise the function's name (stored in the function object) will be used.
python
- grouped['D'].agg({'result1' : np.sum,
'result2' : np.mean})
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:
python
grouped.agg([np.sum, np.mean, np.std])
Passing a dict of functions has different behavior by default, see the next section.
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:
python
- grouped.agg({'C' : np.sum,
'D' : lambda x: np.std(x, ddof=1)})
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
<groupby.dispatch>:
python
grouped.agg({'C' : 'sum', 'D' : 'std'})
Note
If you pass a dict to aggregate, the ordering of the output colums is non-deterministic. If you want to be sure the output columns will be in a specific order, you can use an OrderedDict. Compare the output of the following two commands:
python
grouped.agg({'D': 'std', 'C': 'mean'}) grouped.agg(OrderedDict([('D', 'std'), ('C', 'mean')]))
Some common aggregations, currently only sum, mean, std, and sem, have optimized Cython implementations:
python
df.groupby('A').sum() df.groupby(['A', 'B']).mean()
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. Thus, the passed transform function should return a result that is the same size as the group chunk. For example, suppose we wished to standardize the data within each group:
python
index = pd.date_range('10/1/1999', periods=1100) ts = pd.Series(np.random.normal(0.5, 2, 1100), index) ts = ts.rolling(window=100,min_periods=100).mean().dropna()
ts.head() ts.tail() key = lambda x: x.year zscore = lambda x: (x - x.mean()) / x.std() transformed = ts.groupby(key).transform(zscore)
We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check:
python
# Original Data grouped = ts.groupby(key) grouped.mean() grouped.std()
# Transformed Data grouped_trans = transformed.groupby(key) grouped_trans.mean() grouped_trans.std()
We can also visually compare the original and transformed data sets.
python
compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})
@savefig groupby_transform_plot.png compare.plot()
Another common data transform is to replace missing data with the group mean.
python
cols = ['A', 'B', 'C'] values = np.random.randn(1000, 3) values[np.random.randint(0, 1000, 100), 0] = np.nan values[np.random.randint(0, 1000, 50), 1] = np.nan values[np.random.randint(0, 1000, 200), 2] = np.nan data_df = pd.DataFrame(values, columns=cols)
python
data_df
countries = np.array(['US', 'UK', 'GR', 'JP']) key = countries[np.random.randint(0, 4, 1000)]
grouped = data_df.groupby(key)
# Non-NA count in each group grouped.count()
f = lambda x: x.fillna(x.mean())
transformed = grouped.transform(f)
We can verify that the group means have not changed in the transformed data and that the transformed data contains no NAs.
python
grouped_trans = transformed.groupby(key)
grouped.mean() # original group means grouped_trans.mean() # transformation did not change group means
grouped.count() # original has some missing data points grouped_trans.count() # counts after transformation grouped_trans.size() # Verify non-NA count equals group size
Note
Some functions when applied to a groupby object will automatically transform the input, 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.
python
grouped.ffill()
0.18.1
Working with the resample, expanding or rolling operations on the groupby level used to require the application of helper functions. However, now it is possible to use resample(), expanding() and rolling() as methods on groupbys.
The example below will apply the rolling() method on the samples of the column B based on the groups of column A.
python
- df = pd.DataFrame({'A': [1] * 10 + [5] * 10,
'B': np.arange(20)})
df
df.groupby('A').rolling(4).B.mean()
The expanding() method will accumulate a given operation (sum() in the example) for all the members of each particular group.
python
df.groupby('A').expanding().sum()
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.
python
- df = 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')
df
df.groupby('group').resample('1D').ffill()
0.12
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.
python
sf = pd.Series([1, 1, 2, 3, 3, 3]) sf.groupby(sf).filter(lambda x: x.sum() > 2)
The argument of filter must be a function that, applied to the group as a whole, returns True or False.
Another useful operation is filtering out elements that belong to groups with only a couple members.
python
dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')}) dff.groupby('B').filter(lambda x: len(x) > 2)
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.
python
dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False)
For DataFrames with multiple columns, filters should explicitly specify a column as the filter criterion.
python
dff['C'] = np.arange(8) dff.groupby('B').filter(lambda x: len(x['C']) > 2)
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 as_index=False will not affect these transformation methods.
For example: head, tail.
python
dff.groupby('B').head(2)
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:
python
grouped = df.groupby('A') grouped.agg(lambda x: x.std())
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:
python
grouped.std()
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:
python
- tsdf = pd.DataFrame(np.random.randn(1000, 3),
index=pd.date_range('1/1/2000', periods=1000), columns=['A', 'B', 'C'])
tsdf.ix[::2] = np.nan grouped = tsdf.groupby(lambda x: x.year) grouped.fillna(method='pad')
In this example, we chopped the collection of time series into yearly chunks then independently called fillna <missing_data.fillna> on the groups.
0.14.1
The nlargest and nsmallest methods work on Series style groupbys:
python
s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3]) g = pd.Series(list('abababab')) gb = s.groupby(g) gb.nlargest(3) gb.nsmallest(3)
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:
python
df grouped = df.groupby('A')
# could also just call .describe() grouped['C'].apply(lambda x: x.describe())
The dimension of the returned result can also change:
In [8]: grouped = df.groupby('A')['C']
- In [10]: def f(group):
....: return pd.DataFrame({'original' : group, ....: 'demeaned' : group - group.mean()}) ....:
In [11]: grouped.apply(f)
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
python
- def f(x):
return pd.Series([ x, x**2 ], index = ['x', 'x^2'])
s = pd.Series(np.random.rand(5)) s s.apply(f)
Note
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.
Warning
In the current implementation apply calls func twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first group.
python
d = pd.DataFrame({"a":["x", "y"], "b":[1,2]}) def identity(df): print df return df
d.groupby("a").apply(identity)
Again consider the example DataFrame we've been looking at:
python
df
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:
python
df.groupby('A').std()
If there are any NaN or NaT values in the grouping key, these will be automatically excluded. So 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:
python
data = pd.Series(np.random.randn(100))
factor = pd.qcut(data, [0, .25, .5, .75, 1.])
data.groupby(factor).mean()
You may need to specify a bit more data to properly group. You can use the pd.Grouper to provide this local control.
python
import datetime
- 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), ] })
df
Groupby a specific column with the desired frequency. This is like resampling.
python
df.groupby([pd.Grouper(freq='1M',key='Date'),'Buyer']).sum()
You have an ambiguous specification in that you have a named index and a column that could be potential groupers.
python
df = df.set_index('Date') df['Date'] = df.index + pd.offsets.MonthEnd(2) df.groupby([pd.Grouper(freq='6M',key='Date'),'Buyer']).sum()
df.groupby([pd.Grouper(freq='6M',level='Date'),'Buyer']).sum()
Just like for a DataFrame or Series you can call head and tail on a groupby:
python
df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) df
g = df.groupby('A') g.head(1)
g.tail(1)
This shows the first or last n rows from each group.
Warning
Before 0.14.0 this was implemented with a fall-through apply, so the result would incorrectly respect the as_index flag:
>>> g.head(1): # was equivalent to g.apply(lambda x: x.head(1))
A B
A
1 0 1 2
5 2 5 6To select from a DataFrame or Series the nth item, use the nth method. This is a reduction method, and will return a single row (or no row) per group if you pass an int for n:
python
df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) g = df.groupby('A')
g.nth(0) g.nth(-1) g.nth(1)
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, for a Series this just needs to be truthy.
python
# nth(0) is the same as g.first() g.nth(0, dropna='any') g.first()
# nth(-1) is the same as g.last() g.nth(-1, dropna='any') # NaNs denote group exhausted when using dropna g.last()
g.B.nth(0, dropna=True)
As with other methods, passing as_index=False, will achieve a filtration, which returns the grouped row.
python
df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B']) g = df.groupby('A',as_index=False)
g.nth(0) g.nth(-1)
You can also select multiple rows from each group by specifying multiple nth values as a list of ints.
python
business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B') df = pd.DataFrame(1, index=business_dates, columns=['a', 'b']) # get the first, 4th, and last date index for each month df.groupby((df.index.year, df.index.month)).nth([0, 3, -1])
0.13.0
To see the order in which each row appears within its group, use the cumcount method:
python
df = pd.DataFrame(list('aaabba'), columns=['A']) df
df.groupby('A').cumcount()
df.groupby('A').cumcount(ascending=False) # kwarg only
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.
python
np.random.seed(1234) df = pd.DataFrame(np.random.randn(50, 2)) df['g'] = np.random.choice(['A', 'B'], size=50) df.loc[df['g'] == 'B', 1] += 3
We can easily visualize this with a boxplot:
python
@savefig groupby_boxplot.png df.groupby('g').boxplot()
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<visualization.box> for more.
Warning
For historical reasons, df.groupby("g").boxplot() is not equivalent to df.boxplot(by="g"). See here<visualization.box.return> for an explanation.
Regroup columns of a DataFrame according to their sum, and sum the aggregated ones.
python
df = pd.DataFrame({'a':[1,0,0], 'b':[0,1,0], 'c':[1,0,0], 'd':[2,3,4]}) df df.groupby(df.sum(), axis=1).sum()
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 get's 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.
python
df = pd.DataFrame(np.random.randn(10,2)) df df.index // 5 df.groupby(df.index // 5).std()
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:
python
- 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], })
- def compute_metrics(x):
result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()} return pd.Series(result, name='metrics')
result = df.groupby('a').apply(compute_metrics)
result
result.stack()