Pandas---统计计算和描述

首先创建DataFrame

import pandas as pd
import numpy as np

df_obj = pd.DataFrame(np.random.randn(5,4),columns = ['a','b','c','d'])
print(df_obj)

运行结果：

          a         b         c         d
0 -1.642584  0.647451  0.559574  0.501999
1  1.363831  2.692271  0.569345  0.698085
2 -0.171346  0.528494 -2.877623  0.225033
3 -0.284563 -0.946625  0.148989  0.627597
4  2.797140 -0.841167  1.037480  0.947025

常用的统计计算函数：
sum, mean, max, min, …
axis=0，按列统计，axis=1，按行统计
skipna排除缺失值，默认为True

# coding:utf-8
import pandas as pd
import numpy as np

df_obj = pd.DataFrame(np.random.randn(5,4),columns = ['a','b','c','d'])

print(df_obj)

print("*"*100)

print(df_obj.max())

print(df_obj.min(axis=1,skipna=False))

print(df_obj.sum())

          a         b         c         d
0  2.005623  0.761594 -0.548926 -1.201357
1  0.407529 -0.218784  0.930699 -0.823741
2  0.641325 -2.037026 -0.518321  0.597472
3  1.112061  0.133388  1.968800 -1.153320
4 -0.032120 -0.774064 -0.467220  1.095355
****************************************************************************************************
a    2.005623
b    0.761594
c    1.968800
d    1.095355
dtype: float64
0   -1.201357
1   -0.823741
2   -2.037026
3   -1.153320
4   -0.774064
dtype: float64
a    4.134417
b   -2.134893
c    1.365031
d   -1.485592
dtype: float64

常用的统计描述

# coding:utf-8
import pandas as pd
import numpy as np

df_obj = pd.DataFrame(np.random.randn(5,4),columns = ['a','b','c','d'])
print(df_obj)
print(df_obj.describe())

运行结果：


          a         b         c         d
0  1.394588 -0.047070 -0.327120  0.218114
1  0.159974  0.667859  0.614309  1.634314
2 -0.372147 -0.966839  0.443205 -1.086333
3  0.026549 -0.959392  0.406259  0.684068
4 -0.838770  2.605669 -1.477656 -1.420096
              a         b         c         d
count  5.000000  5.000000  5.000000  5.000000
mean   0.074039  0.260045 -0.068201  0.006013
std    0.834535  1.479430  0.866901  1.263241
min   -0.838770 -0.966839 -1.477656 -1.420096
25%   -0.372147 -0.959392 -0.327120 -1.086333
50%    0.026549 -0.047070  0.406259  0.218114
75%    0.159974  0.667859  0.443205  0.684068
max    1.394588  2.605669  0.614309  1.634314

常用的统计描述方法：

Pandas---统计计算和描述