Pandas 按工作日分组 (M/T/W/T/F/S/S)
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Pandas group by weekday (M/T/W/T/F/S/S)
提问by mannaroth
I have a pandas dataframe containing a time series (as index) of the form YYYY-MM-DD ('arrival_date') and I'd like to group by each of the weekdays (Monday to Sunday) in order to calculate for the other columns the mean, median, std etc. I should have in the end only seven rows and so far I've only found out how to group by week, which aggregates everything weekly.
我有一个包含 YYYY-MM-DD ('arrival_date') 形式的时间序列（作为索引）的Pandas数据框，我想按每个工作日（周一至周日）进行分组，以便计算另一个列平均值、中位数、标准等。我最后应该只有七行，到目前为止我只发现了如何按周分组，每周汇总所有内容。
# Reading the data
df_data = pd.read_csv('data.csv', delimiter=',')
# Providing the correct format for the data
df_data = pd.to_datetime(df_data['arrival_date'], format='%Y%m%d')
# Converting the time series column to index
df_data.index = pd.to_datetime(df_data['arrival_date'], unit='d')
# Grouping by week (= ~52 rows per year)
week_df = df_data.resample('W').mean()
Is there a simple way to achieve my goal in pandas? I was thinking to choose every other 7th element and perform operations on the resulting array, but that seems unnecessarily complex.
有没有一种简单的方法可以在Pandas中实现我的目标？我想选择每隔 7 个元素并对结果数组执行操作，但这似乎不必要地复杂。
The head of the data frame looks like this
数据框的头部看起来像这样
arrival_date price 1 price_2 price_3 price_4
2 20170816 75.945298 1309.715056 71.510215 22.721958
3 20170817 68.803269 1498.639663 64.675232 22.759137
4 20170818 73.497144 1285.122022 65.620260 24.381532
5 20170819 78.556828 1377.318509 74.028607 26.882429
6 20170820 57.092189 1239.530625 51.942213 22.056378
7 20170821 76.278975 1493.385548 74.801641 27.471604
8 20170822 79.006604 1241.603185 75.360606 28.250994
9 20170823 76.097351 1243.586084 73.459963 24.500618
10 20170824 64.860259 1231.325899 63.205554 25.015120
11 20170825 70.407325 975.091107 64.180692 27.177654
12 20170826 87.742284 1351.306100 79.049023 27.860549
13 20170827 58.014005 1208.424489 51.963388 21.049374
14 20170828 65.774114 1289.341335 59.922912 24.481232
回答by jezrael
I believe you need first parameter parse_datesin read_csvfor parse column to datetime and then groupbyby weekday_nameand aggregate:
我相信你需要第一个参数parse_dates中read_csv的解析列于日期时间，然后groupby通过weekday_name和汇总：
df_data = pd.read_csv('data.csv', parse_dates=['arrival_date'])
week_df = df_data.groupby(df_data['arrival_date'].dt.weekday_name).mean()
print (week_df)
price_1 price_2 price_3 price_4
arrival_date
Friday 71.952235 1130.106565 64.900476 25.779593
Monday 71.026544 1391.363442 67.362277 25.976418
Saturday 83.149556 1364.312304 76.538815 27.371489
Sunday 57.553097 1223.977557 51.952801 21.552876
Thursday 66.831764 1364.982781 63.940393 23.887128
Tuesday 79.006604 1241.603185 75.360606 28.250994
Wednesday 76.021324 1276.650570 72.485089 23.611288
For numeric index use weekday:
对于数字索引使用weekday：
week_df = df_data.groupby(df_data['arrival_date'].dt.weekday).mean()
print (week_df)
price_1 price_2 price_3 price_4
arrival_date
0 71.026544 1391.363442 67.362277 25.976418
1 79.006604 1241.603185 75.360606 28.250994
2 76.021324 1276.650570 72.485089 23.611288
3 66.831764 1364.982781 63.940393 23.887128
4 71.952235 1130.106565 64.900476 25.779593
5 83.149556 1364.312304 76.538815 27.371489
6 57.553097 1223.977557 51.952801 21.552876
EDIT:
编辑：
For correct ordering add reindex:
对于正确的订购添加reindex：
days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday']
week_df = df_data.groupby(df_data['arrival_date'].dt.weekday_name).mean().reindex(days)
print (week_df)
price_1 price_2 price_3 price_4
arrival_date
Monday 71.026544 1391.363442 67.362277 25.976418
Tuesday 79.006604 1241.603185 75.360606 28.250994
Wednesday 76.021324 1276.650570 72.485089 23.611288
Thursday 66.831764 1364.982781 63.940393 23.887128
Friday 71.952235 1130.106565 64.900476 25.779593
Saturday 83.149556 1364.312304 76.538815 27.371489
Sunday 57.553097 1223.977557 51.952801 21.552876
