Pandas group by weekday（M/T/W/T/F/S/S）

我有一個包含YYYY-MM-DD（'arrival_date'）形式的時間序列（作爲索引）的熊貓數據幀和I我想每個星期一到星期天都要分組，以便計算其他列的平均值，中位數，標準偏差等等。我最終應該只有七行，到目前爲止我只知道如何按周分組，每週彙總一切。Pandas group by weekday（M/T/W/T/F/S/S）

# 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()

有一個簡單的方法來實現我的目標，大熊貓？我正在考慮選擇每個其他第7個元素，並對結果數組執行操作，但這似乎不必要的複雜。

數據幀的頭部看起來像這樣

 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

來源

2017-12-18 mannaroth

你可以添加數據樣本？ – jezrael

我相信你需要第一個參數parse_dates在read_csv用於解析列於日期時間，然後通過weekday_name和彙總groupby：

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

對於數字索引使用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

編輯：

對於正確的順序添加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

來源

2017-12-18 08:43:06 jezrael

我知道這不是它的地方，但我想感謝您的驚人快速，簡明和明確的答案！ – mannaroth

歡迎您！美好的一天！ – jezrael

有什麼明顯的原因是天的順序是：「星期五/星期一/星期六/ ......」？ – mannaroth

Pandas group by weekday（M/T/W/T/F/S/S）

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