import pandas as pd
import numpy as np
s = pd.Series([1,2,3,4,np.nan,6,8])
s

0    1.0
1    2.0
2    3.0
3    4.0
4    NaN
5    6.0
6    8.0
dtype: float64

1 dates = pd.date_range(‘20210712‘,periods=6)
2 dates

DatetimeIndex([‘2021-07-12‘, ‘2021-07-13‘, ‘2021-07-14‘, ‘2021-07-15‘,
               ‘2021-07-16‘, ‘2021-07-17‘],
              dtype=‘datetime64[ns]‘, freq=‘D‘)

1 df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list(‘ABCD‘))
2 df

                           A          B           C         D
2021-07-12    0.547980    0.504086    -0.341964    0.517595
2021-07-13    -1.359114    0.859277    -0.174702    -1.156648
2021-07-14    -0.695423    -0.442083    0.135932    0.295130
2021-07-15    0.590984    0.292082    0.780524    0.036832
2021-07-16    0.320222    0.182605    0.988981    0.864780
2021-07-17    -0.193702    0.645405    0.704703    0.680967

df.head(3)# 查看前3行代码
df.tail() # 查看后5行代码

df.index

DatetimeIndex([‘2021-07-12‘, ‘2021-07-13‘, ‘2021-07-14‘, ‘2021-07-15‘,
               ‘2021-07-16‘, ‘2021-07-17‘],
              dtype=‘datetime64[ns]‘, freq=‘D‘)

df.columns

Index([‘A‘, ‘B‘, ‘C‘, ‘D‘], dtype=‘object‘)

df.describe()# 快速查看数据的情况

df.sort_values(by=‘B‘) # 根据某列排序，这里显示错行了

                               A            B          C         D
2021-07-14    -0.695423    -0.442083    0.135932    0.295130
2021-07-16    0.320222    0.182605    0.988981    0.864780
2021-07-15    0.590984    0.292082    0.780524    0.036832
2021-07-12    0.547980    0.504086    -0.341964    0.517595
2021-07-17    -0.193702    0.645405    0.704703    0.680967
2021-07-13    -1.359114    0.859277    -0.174702    -1.156648    

df[‘A‘]# 取第1列的值

2021-07-12    0.547980
2021-07-13   -1.359114
2021-07-14   -0.695423
2021-07-15    0.590984
2021-07-16    0.320222
2021-07-17   -0.193702
Freq: D, Name: A, dtype: float64

df[0:3]# 切片

A    B    C    D
2021-07-12    0.547980    0.504086    -0.341964    0.517595
2021-07-13    -1.359114    0.859277    -0.174702    -1.156648
2021-07-14    -0.695423    -0.442083    0.135932    0.295130