Pandas聚合運(yùn)算和分組運(yùn)算的實(shí)現(xiàn)示例

更新時(shí)間：2019年10月17日 11:22:05 作者：CHZZBECK

這篇文章主要介紹了Pandas聚合運(yùn)算和分組運(yùn)算的實(shí)現(xiàn)示例，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

1.聚合運(yùn)算

(1)使用內(nèi)置的聚合運(yùn)算函數(shù)進(jìn)行計(jì)算

1>內(nèi)置的聚合運(yùn)算函數(shù)

sum(),mean(),max(),min(),size(),describe()...等等

2>應(yīng)用聚合運(yùn)算函數(shù)進(jìn)行計(jì)算

import numpy as np
import pandas as pd
#創(chuàng)建df對(duì)象
dict_data = {
  'key1':['a','b','c','d','a','b','c','d'],
  'key2':['one','two','three','one','two','three','one','two'],
  'data1':np.random.randint(1,10,8),
  'data2':np.random.randint(1,10,8)
}
 
df = pd.DataFrame(dict_data)
print(df)
'''
  data1 data2 key1  key2
0   3   4  a  one
1   7   9  b  two
2   5   7  c three
3   3   4  d  one
4   8   7  a  two
5   4   7  b three
6   8   9  c  one
7   4   4  d  two
'''
#根據(jù)key1分組,進(jìn)行sum()運(yùn)算
df = df.groupby('key1').sum()
print(df)
'''
key1       
a    12   10
b     8   5
c     8   11
d    16   13
'''
#內(nèi)置的聚合函數(shù)
print(df.groupby('key1').sum())
print('*'*50)
print(df.groupby('key1').max())
print('*'*50)
print(df.groupby('key1').min())
print('*'*50)
print(df.groupby('key1').mean())
print('*'*50)
print(df.groupby('key1').size())
print('*'*50)
#分組中非Nan數(shù)據(jù)的數(shù)量
print(df.groupby('key1').count())
print('*'*50)
print(df.groupby('key1').describe())

(2)自定義聚合函數(shù)進(jìn)行計(jì)算

在使用自定義聚合函數(shù)的時(shí)候,需要用到一個(gè)agg()函數(shù)

#自定義聚合函數(shù)
#最大值-最小值
def peak_range(df):
  #返回?cái)?shù)據(jù)范圍差值
  return df.max()**2 - df.min()**2
 
#agg() 可以將聚合計(jì)算的結(jié)果祖闖成一個(gè)dataframe對(duì)象返回
 
print(df.groupby('key1').agg(peak_range))
 
#lambda
print(df.groupby('key1').agg(lambda df:df.max()-df.min()))

(3)應(yīng)用多個(gè)聚合函數(shù),默認(rèn)列索引為函數(shù)名

#應(yīng)用多個(gè)聚合函數(shù),默認(rèn)列索引為函數(shù)名
#通過元素重新命名列索引('列索引',函數(shù))
print(df.groupby('key1').agg(['sum','std','mean',('range',peak_range)]))
'''
   data1           data2           
    sum    std mean range  sum    std mean range
key1                           
a    10 2.828427 5.0  40  12 2.828427 6.0  48
b    10 5.656854 5.0  80   8 1.414214 4.0  16
c    6 1.414214 3.0  12   9 0.707107 4.5   9
d    15 0.707107 7.5  15   8 2.828427 4.0  32
'''

(4)指定每一列使用某個(gè)聚合運(yùn)算函數(shù)

#指定每一列使用某個(gè)聚合運(yùn)算函數(shù)
print(df.groupby('key1').agg({'data1':'mean','data2':'sum'}))
'''
   data1 data2
key1       
a    5.0   12
b    5.0   8
c    3.0   9
d    7.5   8
'''

2.分組運(yùn)算

(1)進(jìn)行分組運(yùn)算,并在運(yùn)算后的結(jié)果列索引前加前綴

加前綴用到add_prefix('前綴')函數(shù)

#創(chuàng)建df對(duì)象
dict_data = {
  'key1':['a','b','c','d','a','b','c','d'],
  'key2':['one','two','three','one','two','three','one','two'],
  'data1':np.random.randint(1,10,8),
  'data2':np.random.randint(1,10,8)
}
 
df = pd.DataFrame(dict_data)
print(df)
'''
  data1 data2 key1  key2
0   1   5  a  one
1   9   3  b  two
2   3   6  c three
3   6   9  d  one
4   8   4  a  two
5   5   5  b three
6   9   6  c  one
7   4   1  d  two
'''
#按照key1分組,進(jìn)行sum()運(yùn)算
#在運(yùn)算結(jié)果的列索引前添加前綴
k1_sum = df.groupby('key1').sum().add_prefix('sum_')
print(k1_sum)
'''
   sum_data1 sum_data2
key1           
a       9     9
b      14     8
c      12     12
d      10     10
'''

(2)進(jìn)行分組運(yùn)算,并把原始數(shù)據(jù)和結(jié)果數(shù)據(jù)合并

#創(chuàng)建df對(duì)象
dict_data = {
  'key1':['a','b','c','d','a','b','c','d'],
  'key2':['one','two','three','one','two','three','one','two'],
  'data1':np.random.randint(1,10,8),
  'data2':np.random.randint(1,10,8)
}
 
df = pd.DataFrame(dict_data)
print(df)
'''
  data1 data2 key1  key2
0   1   5  a  one
1   9   3  b  two
2   3   6  c three
3   6   9  d  one
4   8   4  a  two
5   5   5  b three
6   9   6  c  one
7   4   1  d  two
'''
#按照key1分組,進(jìn)行sum()運(yùn)算
#在運(yùn)算結(jié)果的列索引前添加前綴
k1_sum = df.groupby('key1').sum().add_prefix('sum_')
print(k1_sum)
'''
   sum_data1 sum_data2
key1           
a       9     9
b      14     8
c      12     12
d      10     10
'''
#將運(yùn)算結(jié)果和原始數(shù)據(jù)拼接到一起
#參數(shù)1:原始數(shù)據(jù)
#參數(shù)2:運(yùn)算結(jié)果數(shù)據(jù)
pd.merge(df,k1_sum,left_on='key1',right_index=True)

(3)使用transform()函數(shù),將計(jì)算結(jié)果按照原始數(shù)據(jù)排序成一個(gè)DataFrame對(duì)象

#創(chuàng)建df對(duì)象
dict_data = {
  'key1':['a','b','c','d','a','b','c','d'],
  'key2':['one','two','three','one','two','three','one','two'],
  'data1':np.random.randint(1,10,8),
  'data2':np.random.randint(1,10,8)
}
 
df = pd.DataFrame(dict_data)
print(df)
'''
  data1 data2 key1  key2
0   1   5  a  one
1   9   3  b  two
2   3   6  c three
3   6   9  d  one
4   8   4  a  two
5   5   5  b three
6   9   6  c  one
7   4   1  d  two
'''
#按照key1分組,進(jìn)行sum()運(yùn)算
#在運(yùn)算結(jié)果的列索引前添加前綴
k1_sum = df.groupby('key1').sum().add_prefix('sum_')
print(k1_sum)
'''
   sum_data1 sum_data2
key1           
a       9     9
b      14     8
c      12     12
d      10     10
'''
#transform() 計(jì)算 會(huì)將計(jì)算的結(jié)果按照原始數(shù)據(jù)的排序組裝成一個(gè)dataframe對(duì)象
k1_sum_tf = df.groupby('key1').transform(np.sum).add_prefix('sum_')
# print(k1_sum_tf.columns)
 
#把運(yùn)算結(jié)果數(shù)據(jù)拼接到原始數(shù)據(jù)后
df[k1_sum_tf.columns] = k1_sum_tf
print(df)
'''
  data1 data2 key1  key2 sum_data1 sum_data2 sum_key2
0   5   4  a  one     9    12  onetwo
1   3   3  b  two     5    12 twothree
2   9   2  c three    14     9 threeone
3   6   5  d  one    11     9  onetwo
4   4   8  a  two     9    12  onetwo
5   2   9  b three     5    12 twothree
6   5   7  c  one    14     9 threeone
7   5   4  d  two    11     9  onetwo
'''

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