pandas读取CSV文件时查看修改各列的数据类型格式

print(Array.dtype)
#输出int64
print(df.dtypes)
#输出Df下所有列的数据格式 a:int64,b:int64

import pandas as pd
import numpy as np
df = pd.read_csv('000917.csv',encoding='gbk')
df = df[df['涨跌幅']!='None']
df['涨跌幅'] = df['涨跌幅'].astype(np.float64)

print(df[df['涨跌幅']>5])

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a)

df = pd.DataFrame(a, dtype='float') #示例1
df = pd.DataFrame(data=d, dtype=np.int8) #示例2
df = pd.read_csv("somefile.csv", dtype = {'column_name' : str})

>>> s = pd.Series(['1', '2', '4.7', 'pandas', '10'])
>>> s
0     1
1     2
2    4.7
3  pandas
4    10
dtype: object

>>> pd.to_numeric(s) # or pd.to_numeric(s, errors='raise')
ValueError: Unable to parse string

>>> pd.to_numeric(s, errors='coerce')
0   1.0
1   2.0
2   4.7
3   NaN
4  10.0
dtype: float64

>>> pd.to_numeric(s, errors='ignore')
# the original Series is returned untouched

>>> a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
>>> df = pd.DataFrame(a, columns=['col1','col2','col3'])
>>> df
 col1 col2 col3
0  a 1.2  4.2
1  b  70 0.03
2  x  5   0

df[['col2','col3']] = df[['col2','col3']].apply(pd.to_numeric)

df.apply(pd.to_numeric, errors='ignore')

>>> df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
>>> df.dtypes
a  object
b  object
dtype: object

>>> df = df.infer_objects()
>>> df.dtypes
a   int64
b  object
dtype: object

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['one', 'two', 'three'])
df
Out[16]: 
 one two three
0  a 1.2  4.2
1  b  70 0.03
2  x  5   0
df.dtypes
Out[17]: 
one   object
two   object
three  object
df[['two', 'three']] = df[['two', 'three']].astype(float)
df.dtypes
Out[19]: 
one    object
two   float64
three  float64