下面的代码部分包含两个函数。 df_sample() 创建所需大小、起点和列名的数据框。函数 multiJoin() 采用预定义的数据帧列表，并使用可用于pandas Join 的任何方法连接它们。使用该设置，您需要做的就是运行multiJoin(dfs = [df1, df2, df3], method = 'outer', names = ['Apple', 'Amazon', 'SomeOther']) 以获得示例数据帧的所需结果。我添加了一个函数newNames(df, sep, name1, name2) 来处理分层列名：
Apple Amazon
Open High Low Close Volume Open High Low Close Volume
# imports
import pandas as pd
import numpy as np
np.random.seed(1234)
# Function for reproducible data sample
def df_sample(start, rows, names):
''' Function to create data sample with random returns
Parameters
==========
rows : number of rows in the dataframe
names: list of names to represent assets
Example
=======
>>> returns(rows = 2, names = ['A', 'B'])
A B
2017-01-01 0.0027 0.0075
2017-01-02 -0.0050 -0.0024
'''
listVars= names
rng = pd.date_range(start, periods=rows, freq='D')
df_temp = pd.DataFrame(np.random.randint(-100,200,size=(rows, len(listVars))), columns=listVars)
df_temp = df_temp.set_index(rng)
#df_temp = df_temp / 10000
return df_temp
colNames = ['Open', 'High', 'Low', 'Close']
# Reproducible dataframes
df1 = df_sample('1/1/2017', 150,colNames)
df2 = df_sample('2/1/2017', 150,colNames)
df3 = df_sample('3/1/2017', 150,colNames)
#%%
def multiJoin(dfs, method, names):
""" Takes a pre-defined list of pandas dataframes and joins them
by the method specified and available in df.join().
This is a specific case for joining a bunch og OHLCV tables,
so column names will overlap. You should therefore specify
a list for each dataframe to provide unique column names.
Joining dataframes with different indexes will result in
omitted and / or missing data.
Using method = 'outer' will display missing values for mismatching dates.
Using method = 'inner' will keep only dates where all dataframes have values and omit
all other.
"""
# Isolate a df to join all other dfs on
df_left = dfs[0]
df_left.columns = [names[0]+ '_' + col for col in df_left.columns]
df_other = dfs[1:]
# Manage names
names_other = names[1:]
# Loop through list of dataframes to join on the first one,
# and rename columns
counter = 0
for df in df_other:
df.columns = [names_other[counter] + '_' + col for col in df.columns]
df_left = df_left.join(df, how = method)
counter = counter + 1
return df_left
dfJoined_outer = multiJoin(dfs = [df1, df2, df3], method = 'outer', names = ['Apple', 'Amazon', 'SomeOther'])
输出：
如果你运行dfJoined_inner = multiJoin(dfs = [df1, df2, df3], method = 'inner', names = ['Apple', 'Amazon', 'SomeOther'])，你会得到：
考虑到OP的评论后添加：
我添加了一个基于pandas.MultiIndex.from_arrays 的函数，它将为您提供分层列名称，使数据框看起来就像您所要求的那样。只需运行df_multi = newNames(df = dfJoined_inner, sep = '_')。
def newNames(df, sep, name1, name2):
""" Takes a single column index from a pandas dataframe,
splits the original titles by a specified separator,
and replaces the single column index with a
multi index. You can also assign names to levels of your new index
"""
df_temp = dfJoined_inner
sep = '_'
single = pd.Series(list(df_temp))
multi= single.str.split(sep, expand = True)
multiIndex = pd.MultiIndex.from_arrays((multi[0], multi[1]), names = (name1, name2))
df_new = pd.DataFrame(df_temp.values, index = df_temp.index, columns = multiIndex)
return(df_new)
df_multi = newNames(df = dfJoined_inner, sep = '_', name1 = 'Stock', name2 = 'Category')
我使用的是 Spyder，因此变量资源管理器中数据框的屏幕截图如下所示（注意列标题中的括号）：
但是如果你运行print(df_multi.tail())，你会看到列标题看起来就像你请求的那样：
#Output
Stock Apple Amazon SomeOther
Category Open High Low Close Open High Low Close Open High Low Close
2017-05-26 -92 140 47 -53 -73 -50 -94 -72 16 115 96 74
2017-05-27 169 -34 -78 120 46 195 28 186 -9 102 -13 141
2017-05-28 -98 -10 57 151 169 -17 148 150 -26 -43 -53 63
2017-05-29 1 87 38 0 28 71 52 -57 6 86 179 -6
2017-05-30 -31 52 33 63 46 149 -71 -30 -20 188 -34 -60