数据清洗之 日期格式数据处理

日期格式数据处理

• Pandas中使用to_datetime()方法将文本格式转换为日期格式
• dataframe数据类型如果为datetime64，可以使用dt方法取出年月日等
• 对于时间差数据，可以使用timedelta函数将其转换为指定时间单位的数值
• 时间差数据，可以使用dt方法访问其常用属性

import pandas as pd
import numpy as np
import os

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据转换'

os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

df = pd.read_csv('baby_trade_history.csv', encoding='utf-8', dtype={'user_id':str})

df.head(5)

	user_id	auction_id	cat_id	cat1	property	buy_mount	day
0	786295544	41098319944	50014866	50022520	21458:86755362;13023209:3593274;10984217:21985...	2	20140919
1	532110457	17916191097	50011993	28	21458:11399317;1628862:3251296;21475:137325;16...	1	20131011
2	249013725	21896936223	50012461	50014815	21458:30992;1628665:92012;1628665:3233938;1628...	1	20131011
3	917056007	12515996043	50018831	50014815	21458:15841995;21956:3494076;27000458:59723383...	2	20141023
4	444069173	20487688075	50013636	50008168	21458:30992;13658074:3323064;1628665:3233941;1...	1	20141103

df.info()

RangeIndex: 29971 entries, 0 to 29970
Data columns (total 7 columns):
user_id       29971 non-null object
auction_id    29971 non-null int64
cat_id        29971 non-null int64
cat1          29971 non-null int64
property      29827 non-null object
buy_mount     29971 non-null int64
day           29971 non-null int64
dtypes: int64(5), object(2)
memory usage: 1.6+ MB

df['buy_date'] = pd.to_datetime(df['day'], format='%Y%m%d', errors='coerce')

df.info()

RangeIndex: 29971 entries, 0 to 29970
Data columns (total 8 columns):
user_id       29971 non-null object
auction_id    29971 non-null int64
cat_id        29971 non-null int64
cat1          29971 non-null int64
property      29827 non-null object
buy_mount     29971 non-null int64
day           29971 non-null int64
buy_date      29971 non-null datetime64[ns]
dtypes: datetime64[ns](1), int64(5), object(2)
memory usage: 1.8+ MB

df.head(5)

	user_id	auction_id	cat_id	cat1	property	buy_mount	day	buy_date
0	786295544	41098319944	50014866	50022520	21458:86755362;13023209:3593274;10984217:21985...	2	20140919	2014-09-19
1	532110457	17916191097	50011993	28	21458:11399317;1628862:3251296;21475:137325;16...	1	20131011	2013-10-11
2	249013725	21896936223	50012461	50014815	21458:30992;1628665:92012;1628665:3233938;1628...	1	20131011	2013-10-11
3	917056007	12515996043	50018831	50014815	21458:15841995;21956:3494076;27000458:59723383...	2	20141023	2014-10-23
4	444069173	20487688075	50013636	50008168	21458:30992;13658074:3323064;1628665:3233941;1...	1	20141103	2014-11-03

# 使用dt方法提取属性
# df['buy_date'].dt.year  # 提取年
# df['buy_date'].dt.month  # 提取月
# df['buy_date'].dt.day  # 提取天

df['diff_day'] = pd.datetime.now() - df['buy_date']  # 时间差格式

df.head(5)

	user_id	auction_id	cat_id	cat1	property	buy_mount	day	buy_date	diff_day
0	786295544	41098319944	50014866	50022520	21458:86755362;13023209:3593274;10984217:21985...	2	20140919	2014-09-19	2034 days 22:32:35.614788
1	532110457	17916191097	50011993	28	21458:11399317;1628862:3251296;21475:137325;16...	1	20131011	2013-10-11	2377 days 22:32:35.614788
2	249013725	21896936223	50012461	50014815	21458:30992;1628665:92012;1628665:3233938;1628...	1	20131011	2013-10-11	2377 days 22:32:35.614788
3	917056007	12515996043	50018831	50014815	21458:15841995;21956:3494076;27000458:59723383...	2	20141023	2014-10-23	2000 days 22:32:35.614788
4	444069173	20487688075	50013636	50008168	21458:30992;13658074:3323064;1628665:3233941;1...	1	20141103	2014-11-03	1989 days 22:32:35.614788

df.dtypes

user_id                object
auction_id              int64
cat_id                  int64
cat1                    int64
property               object
buy_mount               int64
day                     int64
buy_date       datetime64[ns]
diff_day      timedelta64[ns]
dtype: object

# 使用dt方法提取属性
# df['diff_day'].dt.days  # 提取天数
# df['diff_day'].dt.seconds  # 提取秒
# df['diff_day'].dt.microseconds  # 提取纳秒

# 将时间差转换为规定的格式
df['时间差'] = df['diff_day']/pd.Timedelta('1 D')  # 转换为天数

df['时间差'].head(5)

0    2034.939301
1    2377.939301
2    2377.939301
3    2000.939301
4    1989.939301
Name: 时间差, dtype: float64

df['时间差'] = df['diff_day']/pd.Timedelta('1 H')  # 转换为小时

df['时间差'].head(5)

0    48838.543226
1    57070.543226
2    57070.543226
3    48022.543226
4    47758.543226
Name: 时间差, dtype: float64

df['时间差'] = df['diff_day']/pd.Timedelta('1 M')  # 转换为分钟数

df['时间差'].head(5)

0    2.930313e+06
1    3.424233e+06
2    3.424233e+06
3    2.881353e+06
4    2.865513e+06
Name: 时间差, dtype: float64

# 将科学计数转换为小数
df['时间差'].head(5).round(decimals=3)

0    2930312.594
1    3424232.594
2    3424232.594
3    2881352.594
4    2865512.594
Name: 时间差, dtype: float64

# D: 天  M: 月  Y: 年
# 转换为天数
df['diff_day'].astype('timedelta64[D]').head(5)

0    2034.0
1    2377.0
2    2377.0
3    2000.0
4    1989.0
Name: diff_day, dtype: float64

# 转换为月数
df['diff_day'].astype('timedelta64[M]').head(5)

0    66.0
1    78.0
2    78.0
3    65.0
4    65.0
Name: diff_day, dtype: float64

# 转换为年数
df['diff_day'].astype('timedelta64[Y]').head(5)

0    5.0
1    6.0
2    6.0
3    5.0
4    5.0
Name: diff_day, dtype: float64

# 转换为小时
# 还可转换为分钟、秒等等
df['diff_day'].astype('timedelta64[h]').head(5)

0    48838.0
1    57070.0
2    57070.0
3    48022.0
4    47758.0
Name: diff_day, dtype: float64