Pandas使用DataFrame进行数据分析比赛进阶之路(二):日期数据处理:按日期筛选、显示及统计数据

qinjianhuang 2020-11-13 05:22:56
pandas 使用 进行 dataframe 行数


首先,表格的数据格式如下:

这里写图片描述

1、获取某年某月数据

data_train = pd.read_csv('data/train.csv')
# 将数据类型转换为日期类型
data_train['date'] = pd.to_datetime(data_train['date'])
# 将date设置为index
df = data_train.set_index('date')
# 获取某年的数据
print(df['2010'].head())
# 获取某月的数据
print(df['2013-11'].head())

输出结果:

 id questions answers
date
2010-10-01 1 742 1561
2010-10-02 2 400 783
2010-10-03 3 388 771
2010-10-04 4 762 1474
2010-10-05 5 821 1639
id questions answers
date
2013-11-01 1128 3401 6858
2013-11-02 1129 2626 5467
2013-11-03 1130 2703 5557
2013-11-04 1131 3602 6941
2013-11-05 1132 3741 7312

2、获取某个时期之前或之后的数据

# 获取某个时期之前或之后的数据
# 获取2014年以后的数据
print(df.truncate(before='2014').head())
# 获取2013-11之前的数据
print(df.truncate(after='2013-11').head())
# 获取2016-02年以后的数据
print(df.truncate(before='2016-02').head())
# 获取2016-02-2年以后的数据
print(df.truncate(before='2016-02-2').head())

输出结果:

 id questions answers
date
2014-01-01 1189 2586 5576
2014-01-02 1190 3541 7175
2014-01-03 1191 3655 7395
2014-01-04 1192 2947 6099
2014-01-05 1193 2847 5935
id questions answers
date
2010-10-01 1 742 1561
2010-10-02 2 400 783
2010-10-03 3 388 771
2010-10-04 4 762 1474
2010-10-05 5 821 1639
id questions answers
date
2016-02-01 1950 5434 10398
2016-02-02 1951 5650 10795
2016-02-03 1952 5744 10879
2016-02-04 1953 5666 10886
2016-02-05 1954 5371 10508
id questions answers
date
2016-02-02 1951 5650 10795
2016-02-03 1952 5744 10879
2016-02-04 1953 5666 10886
2016-02-05 1954 5371 10508
2016-02-06 1955 4296 8800

3、按某个指标显示,但不统计

# 按月显示,但不统计
df_period_M = df.to_period('M').head()
print(df_period_M)
# 按季度显示,但不统计
df_period_Q = df.to_period('Q').head()
print(df_period_Q)
# 按年度显示,但不统计
df_period_A = df.to_period('A').head()
print(df_period_A)

输出结果:

 id questions answers
date
2010-10 1 742 1561
2010-10 2 400 783
2010-10 3 388 771
2010-10 4 762 1474
2010-10 5 821 1639
id questions answers
date
2010Q4 1 742 1561
2010Q4 2 400 783
2010Q4 3 388 771
2010Q4 4 762 1474
2010Q4 5 821 1639
id questions answers
date
2010 1 742 1561
2010 2 400 783
2010 3 388 771
2010 4 762 1474
2010 5 821 1639

4、按某个指标显示,并且统计

# 按年统计并显示
print(df.resample('AS').sum().to_period('A'))
# 按季度统计并显示
print(df.resample('Q').sum().to_period('Q').head())
# 按月度统计并显示
print(df.resample('M').sum().to_period('M').head())
# 按月度统计并显示
print(df.resample('W').sum().to_period('W').head())

输出结果:

 id questions answers
date
2010 4278 74363 153006
2011 100375 535290 1091651
2012 234423 862831 1718434
2013 367190 1179155 2320421
2014 500415 1487677 2876611
2015 633640 1734023 3368264
2016 698810 1808649 3476335
id questions answers
date
2010Q4 4278 74363 153006
2011Q1 12375 105858 217767
2011Q2 20748 127873 260836
2011Q3 29394 144424 293853
2011Q4 37858 157135 319195
id questions answers
date
2010-10 496 22218 44882
2010-11 1395 25418 52841
2010-12 2387 26727 55283
2011-01 3348 31502 65477
2011-02 3850 33240 67627
id questions answers
date
2010-09-27/2010-10-03 6 1530 3115
2010-10-04/2010-10-10 49 4869 9636
2010-10-11/2010-10-17 98 5079 10344
2010-10-18/2010-10-24 147 5361 10847
2010-10-25/2010-10-31 196 5379 10940

附录:日期类型截图

这里写图片描述

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本文为[qinjianhuang]所创,转载请带上原文链接,感谢
https://huangqinjian.blog.csdn.net/article/details/79791190

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