读取数据

读取csv

df = pd.read_csv('x.csv')

读取压缩包

import zipfile

with zipfile.ZipFile('x.csv.zip', 'r') as z:
    f = z.open('x.csv')
    df = pd.read_csv(f, header=0)

保存为csv

out_df.to_csv('predict_result.csv', encoding='utf-8', index=0, header=None)
# index=0即不要序号的列，header=None即不要表头

查看数据

参考Basics section

查看数据类型

df2.dtypes

Out[30]: 
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

查看head和tail

df.head(1)
df.tail(3)

判断是否为空

df.empty

查看index、column和数据

获取表头df.columns

df.index
df.columns
df.values      
df.count

显示数据的快速统计

df.describe()
Out[19]: 
              A         B         C         D
count  6.000000  6.000000  6.000000  6.000000
mean   0.073711 -0.431125 -0.687758 -0.233103
std    0.843157  0.922818  0.779887  0.973118
min   -0.861849 -2.104569 -1.509059 -1.135632
25%   -0.611510 -0.600794 -1.368714 -1.076610
50%    0.022070 -0.228039 -0.767252 -0.386188
75%    0.658444  0.041933 -0.034326  0.461706
max    1.212112  0.567020  0.276232  1.071804

找缺失值

https://blog.csdn.net/u012387178/article/details/52571725

print(df_base_dpd[df_base_dpd.isnull().values == True])