Import pandas as pd Df = pd.DataFrame({'key1':list('aabba'), 'key2': ['one','two','one','two','one'], 'data1': np.random.randn(5), 'data2': np.random.randn(5)}) Df123456 Grouped=df['data1'].groupby(df['key1']) Grouped.mean()12 The above grouping keys are Series, in fact the grouping key can be any array of appropriate length States=np.array(['Ohio','California', 'California', 'Ohio', 'Ohio']) Years=np.array([2005,2005,2006,2005,2006]) Df['data1'].groupby([states,years]).mean()123 Df.groupby('key1').mean()1 It can be seen that there is no key2 column, because df['key2'] is not numeric data, so it is removed from the result. By default, all numeric columns are aggregated, although sometimes they may be filtered into a subset. Iterate over the group For name, group in df.groupby('key1'): Print (name) Print (group)123 It can be seen that name is the value of key1 in groupby, and group is the content to be output. The same reason: For (k1,k2),group in df.groupby(['key1','key2']): Print ('===k1,k2:') Print (k1,k2) Print ('===k3:') Print (group)12345 Operate the contents of group by, such as converting to a dictionary Piece=dict(list(df.groupby('key1'))) Piece {'a': data1 data2 key1 key2 0 -0.233405 -0.756316 a one 1 -0.232103 -0.095894 a two 4 1.056224 0.736629 a one, 'b': data1 data2 key1 key2 2 0.200875 0.598282 b one 3 -1.437782 0.107547 b two} Piece['a']123456789101112 Groupby defaults to grouping on axis=0, and can be grouped on any other axis by setting. Grouped=df.groupby(df.dtypes, axis=1) Dict(list(grouped)) {dtype('float64'): data1 data2 0 -0.233405 -0.756316 1 -0.232103 -0.095894 2 0.200875 0.598282 3 -1.437782 0.107547 4 1.056224 0.736629, dtype('O'): key1 key2 0 a one 1 a two 2 b one 3 b two 4 a one 123456789101112131415 Select one or a group of columns For big data, in many cases, only some columns need to be aggregated. Df.groupby(['key1','key2'])[['data2']].mean() 12 Group by dictionary or series People=pd.DataFrame(np.random.randn(5,5), Columns=list('abcde'), Index=['Joe','Steve', 'Wes', 'Jim', 'Travis']) People.ix[2:3,['b','c']]=np.nan #Set a few nan People123456 Known grouping relationship of columns Mapping={'a':'red', 'b': 'red', 'c': 'blue', 'd': 'blue', 'e': 'red', 'f': 'orange' } By_column=people.groupby(mapping, axis=1) By_column.sum()12345 If you do not add axis=1, only abcde will appear. The same is true for Series Map_series=pd.Series(mapping) Map_series a red b red c blue d blue e red f orange Dtype: object People.groupby(map_series,axis=1).count()123456789101112 Compared to dic or Series, python functions are more creative when defining grouping relationship mapping. Any function that is treated as a grouping key will be called once on each index, and its return value will be used as the group name. Suppose you group by the length of the person's name, just pass in len People.groupby(len).sum() abcde 3 -1.308709 -2.353354 1.585584 2.908360 -1.267162 5 -0.688506 -0.187575 -0.048742 1.491272 -0.636704 6 0.110028 -0.932493 1.343791 -1.928363 -0.36474512 Mixing functions with arrays, lists, dictionaries, and Series is not a problem, because anything will eventually be converted to an array. Key_list=['one','one','one','two','two'] people.groupby([len,key_list]).sum()1 Group by index level The most convenient part of a hierarchical index is that he can aggregate based on the index level. To achieve this, you can enter the level number or name through the level keyword: Columns=pd.MulTIIndex.from_arrays([['US','US','US','JP','JP'],[1,3,5,1,3]],names=['cty' , 'tenor']) Hier_df=pd.DataFrame(np.random.randn(4,5),columns=columns) Hier_df123 Hier_df.groupby(level='cty', axis=1).count()1 Call a custom aggregate function Aggregate operations on Series or DataFrame columns actually use aggregate or call mean, std, and so on. Next we want to use different aggregate functions for different columns, or apply multiple functions at once. Grouped=TIps.groupby(['sex','smoker']) Grouped_pct=grouped['TIp_pct'] #TIp_pct Grouped_pct.agg('mean')# For the statistics described in the 9-1 icon, you can pass the function name directly as a string. #If you pass in a set of functions, the column name of the obtained df will be named 12345 with the corresponding function. The automatically given column name is low. If a list of (name, function) tuples is passed in, the first element of each tuple will be used as the column name of df. For df, you can define a set of functions for all columns, or apply different functions in different columns. If you want to apply different functions to different columns, the specific way is to agg a dictionary that maps from column names to functions. Df can have hierarchical columns only when applying multiple functions to at least one column Aggregation is just one type of grouping operation, which is a special column of data conversion. Transform and apply are more forked. Transform will apply a function to each group and then put the results in the appropriate location. If each group produces a scalar value, the scalar value will be broadcast. Transform is also a special function with strict conditions: the passed function can only produce two kinds of results, either to produce a scalar value that can be broadcast (eg np.mean), or to produce an array of results of the same size. People=pd.DataFrame(np.random.randn(5,5), Columns=list('abcde'), Index=['Joe','Steve', 'Wes', 'Jim', 'Travis']) People 12345 Key=['one','two','one','two','one'] People.groupby(key).mean()12 People.groupby(key).transform(np.mean)1 You can see that there are many values ​​as in Table 2. Def demean(arr): Return arr-arr.mean() Demeaned=people.groupby(key).transform(demean) Demeaned12345 Demeaned.groupby(key).mean()1 The most general groupby method is apply. Tips=pd.read_csv('C:\\Users\\ecaoyng\\Desktop\\work space\\Python\\py_for_analysis_code\\pydata-book-master\\ch08\ips.csv') Tips[:5]12 New generation of a column Tips['tip_pct']=tips['tip']/tips['total_bill'] Tips[:6]12 Select the top 5 tip_pct values ​​based on the grouping Def top(df,n=5,column='tip_pct'): Return df.sort_index(by=column)[-n:] Top(tips,n=6)123 Group the smoker and apply the function Tips.groupby('smoker').apply(top)1 Multi-parameter version Tips.groupby(['smoker','day']).apply(top,n=1,column='total_bill')1 Cut and qcut combined with groupby makes it easy to analyze the bucket or quantile of the dataset. Frame=pd.DataFrame({'data1':np.random.randn(1000), 'data2': np.random.randn(1000)}) Frame[:5]123 Factor=pd.cut(frame.data1,4) Factor[:10] 0 (0.281, 2.00374] 1 (0.281, 2.00374] 2 (-3.172, -1.442) 3 (-1.442, 0.281) 4 (0.281, 2.00374] 5 (0.281, 2.00374] 6 (-1.442, 0.281) 7 (-1.442, 0.281) 8 (-1.442, 0.281) 9 (-1.442, 0.281) Name: data1, dtype: category Categories (4, object): [(-3.172, -1.442] " (-1.442, 0.281) " (0.281, 2.00374] " (2.00374, 3.727]] 123456789101112131415 Def get_stats(group): Return {'min':group.min(),'max':group.max(),'count':group.count(),'mean':group.mean()} Grouped=frame.data2.groupby(factor) Grouped.apply(get_stats).unstack()1234 These are buckets of equal length. To get equal buckets based on the number of samples, use qcut. Buckets of equal length: equal intervals Equal-sized buckets: equal number of data points Grouping=pd.qcut(frame.data1,10,labels=False)#label=false can get the quantile number Grouped=frame.data2.groupby(grouping) Grouped.apply(get_stats).unstack()123
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