python - Pandas 在多索引 DataFrame 中使用 loc 进行赋值

Question

我已经像这样初始化了一个数据框:

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)

现在我从某个地方获取数据流，但为了程序的缘故，让我这样写:

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

但是这是错误的:

ValueError: cannot set using a multi-index selection indexer with a different length than the value

Answer 1

指定要为其分配数据的列名，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，如果您有一个逗号分隔的文件，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