Pandas 使用 loc 在多索引數據幀中進行分配

Question

我已經初始化了一個這樣的數據框：

df = pd.DataFrame(columns=["stockname","timestamp","price","volume"])
df.timestamp = pd.to_datetime(df.timestamp, format = "%Y-%m-%d %H:%M:%S:%f")
df.set_index(['stockname', 'timestamp'], inplace = True)

現在我從某個地方獲取數據流，但為了程序，讓我這樣寫：

filehandle = open("datasource")

for line in filehandle:
    line = line.rstrip()
    data = line.split(",")
    stockname = data[4]
    price = float(data[3])
    timestamp = pd.to_datetime(data[0], format = "%Y-%m-%d %H:%M:%S:%f")
    volume = int(data[6])

    df.loc[stockname, timestamp] = [price, volume]

filehandle.close()

print df

但這給出了錯誤：

ValueError：無法使用長度與值不同的多索引選擇索引器進行設置

Answer 1

指定您要為其分配數據的列名稱，即

df = pd.DataFrame(columns=["a","b","c","d"])
df.set_index(['a', 'b'], inplace = True)

df.loc[('3','4'),['c','d']] = [4,5]

df.loc[('4','4'),['c','d']] = [3,1]

      c    d
a b          
3 4  4.0  5.0
4 4  3.0  1.0

此外，如果您有一個逗號分隔的文件，那么您可以使用read_csv即：

import io
import pandas as pd
st = '''2017-12-08 15:29:58:740657,245.0,426001,248.65,APPL,190342,2075673,249.35,244.2
        2017-12-08 16:29:58:740657,245.0,426001,248.65,GOOGL,190342,2075673,249.35,244.2
        2017-12-08 18:29:58:740657,245.0,426001,248.65,GOOGL,190342,2075673,249.35,244.2
        '''
#instead of `io`, add the source name
df = pd.read_csv(io.StringIO(st),header=None)
# Now set the index and select what you want 
df.set_index([0,4])[[1,7]]

                                   1       7
 0                          4                   
2017-12-08 15:29:58.740657 APPL   245.0  249.35
2017-12-08 16:29:58.740657 GOOGL  245.0  249.35
2017-12-08 18:29:58.740657 GOOGL  245.0  249.35

Answer 2

我認為你正在尋找的是：

df.loc[a,b,:] = [c,d]

這是您的數據框的示例：

for i in range(3):
    for j in range(3):
        df.loc[(str(i),str(j)),:] = [i,j]

輸出：

     c  d
a b      
0 0  0  0
  1  0  1
  2  0  2
1 0  1  0
  1  1  1
  2  1  2
2 0  2  0
  1  2  1
  2  2  2

Answer 3

您可能想使用df.at[index, column_name] = value來逃避此錯誤