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

  • Z时代
  • 2024-01-10
  • 分类：问答

我有一个包含YYYY-MM-DD（'arrival_date'）形式的时间序列（作为索引）的熊猫数据帧和I我想每个星期一到星期天都要分组，以便计算其他列的平均值，中位数，标准偏差等等。我最终应该只有七行，到目前为止我只知道如何按周分组，每周汇总一切。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()

有一个简单的方法来实现我的目标，大熊猫？我正在考虑选择每个其他第7个元素，并对结果数组执行操作，但这似乎不必要的复杂。

数据帧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：

我相信你需要第一个参数parse_dates在read_csv用于解析列于日期时间，然后通过weekday_name和汇总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

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

编辑：

对于正确的顺序添加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

以上是 Pandas group by weekday（M/T/W/T/F/S/S） 的全部内容， 来源链接： utcz.com/qa/261787.html

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