Pandas类型转换astype()的实现

import pandas as pd
 
df = pd.DataFrame([['liver','E',89,21,24,64],
                   ['Arry','C',36,37,37,57],
                   ['Ack','A',57,60,18,84],
                   ['Eorge','C',93,96,71,78],
                   ['Oah','D',65,49,61,86]
                  ], 
                   columns = ['name','team','Q1','Q2','Q3','Q4'])
 
res = df.dtypes
 
df.Q1.astype('int32').dtypes # dtype('int32')
df.astype({'Q1':'int32','Q2':'int32'}).dtypes

# 以下是一些使用示例:
df.index.astype('int64') # 索引类型转换
df.astype('int32') # 所有数据转换为int32
df.astype({'col1':'int32'}) # 指定字段转指定类型
s.astype('int64')
s.astype('int64',copy = False) # 不与原数据关联
df['name'].astype('object')
data['Q4'].astype('float')
s.astype('datatime64[ns]') # 转为时间类型
data['状态'].astype('bool')

import pandas as pd
 
df = pd.DataFrame([['liver','E',89,21,24,64],
                   ['Arry','C',36,37,37,57],
                   ['Ack','A',57,60,18,84],
                   ['Eorge','C',93,96,71,78],
                   ['Oah','D',65,49,61,86]
                  ], 
                   columns = ['name','team','Q1','Q2','Q3','Q4'])
df.dtypes
 
s = pd.Series(['One','Two','Three'])
s.dtype

# 将"89.3%"这样的文本转为浮点数
data.rate.apply(lambda x:x.replace('%','')).astype('float')/100

import pandas as pd
 
# 对所有字段指定统一类型
df = pd.DataFrame(data, dtype = 'float32')
# 对每个字段分别指定
df = pd.read_excel(data, dtype = {'team':'string','Q1':'int32'})