一、数据预览:tail();head()

import numpy as np
import pandas as pd
df_obj = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd'])
print(df_obj.tail())# 数据预览尾巴
print(df_obj.head())# 数据预览头部
          a         b         c         d
0 -0.507788 0.213237 0.003150 -0.777312
1 -0.896653 -2.188016 -0.114848 0.167057
2 -1.131242 -0.142287 -1.027330 1.861814
3 0.369608 0.823453 1.030830 -0.041778
4 -0.647625 0.056791 -0.394078 -1.347718
a b c d
0 -0.507788 0.213237 0.003150 -0.777312
1 -0.896653 -2.188016 -0.114848 0.167057
2 -1.131242 -0.142287 -1.027330 1.861814
3 0.369608 0.823453 1.030830 -0.041778
4 -0.647625 0.056791 -0.394078 -1.347718

二、数据描述:shape;info()

print ('数据集有%i行,%i列' %(df_obj.shape[0], df_obj.shape[1]))
数据集有5行,4
print(df_obj.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 4 columns):
a 5 non-null float64
b 5 non-null float64
c 5 non-null float64
d 5 non-null float64
dtypes: float64(4)
memory usage: 288.0 bytes
None

三、数据统计:describe()

print(df_obj.describe())
              a         b         c         d
count 5.000000 5.000000 5.000000 5.000000
mean -0.562740 -0.247365 -0.100455 -0.027587
std 0.573191 1.143294 0.747673 1.215808
min -1.131242 -2.188016 -1.027330 -1.347718
25% -0.896653 -0.142287 -0.394078 -0.777312
50% -0.647625 0.056791 -0.114848 -0.041778
75% -0.507788 0.213237 0.003150 0.167057
max 0.369608 0.823453 1.030830 1.861814

四、pandas 不完全显示行列

pd.set_option('display.max_rows', 100)        //显示的最大行数(避免只显示部分行数据)
pd.set_option('display.max_columns', 1000) //显示的最大列数(避免列显示不全)
pd.set_option("display.max_colwidth", 1000) //每一列最大的宽度(避免属性值或列名显示不全)
pd.set_option('display.width', 1000) //每一行的宽度(避免换行)