熊猫三向联接列上的多个数据框

问题:熊猫三向联接列上的多个数据框

我有3个CSV文件。每个列都有第一列作为人员的(字符串)名称,而每个数据框中的所有其他列都是该人员的属性。

如何将所有三个CSV文档“连接”在一起以创建一个CSV,而每一行都具有该人的字符串名称的每个唯一值的所有属性?

join()pandas中的函数指定我需要一个多索引,但是我对层次化索引方案与基于单个索引进行联接有何关系感到困惑。

I have 3 CSV files. Each has the first column as the (string) names of people, while all the other columns in each dataframe are attributes of that person.

How can I “join” together all three CSV documents to create a single CSV with each row having all the attributes for each unique value of the person’s string name?

The join() function in pandas specifies that I need a multiindex, but I’m confused about what a hierarchical indexing scheme has to do with making a join based on a single index.


回答 0

假设进口:

import pandas as pd

John Galt的答案基本上是一项reduce手术。如果我有几个数据帧,则将它们放在这样的列表中(通过列表推导或循环或其他方式生成):

dfs = [df0, df1, df2, dfN]

假设它们有一些共同的列,例如name您的示例,我将执行以下操作:

df_final = reduce(lambda left,right: pd.merge(left,right,on='name'), dfs)

这样,您的代码应该可以与要合并的任意数量的数据框一起使用。

编辑2016年8月1日:对于使用Python 3的用户:reduce已移入functools。因此,要使用此功能,您首先需要导入该模块:

from functools import reduce

Assumed imports:

import pandas as pd

John Galt’s answer is basically a reduce operation. If I have more than a handful of dataframes, I’d put them in a list like this (generated via list comprehensions or loops or whatnot):

dfs = [df0, df1, df2, dfN]

Assuming they have some common column, like name in your example, I’d do the following:

df_final = reduce(lambda left,right: pd.merge(left,right,on='name'), dfs)

That way, your code should work with whatever number of dataframes you want to merge.

Edit August 1, 2016: For those using Python 3: reduce has been moved into functools. So to use this function, you’ll first need to import that module:

from functools import reduce

回答 1

如果您有3个数据框,则可以尝试

# Merge multiple dataframes
df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32'])

pd.merge(pd.merge(df1,df2,on='name'),df3,on='name')

或者,如cwharland所述

df1.merge(df2,on='name').merge(df3,on='name')

You could try this if you have 3 dataframes

# Merge multiple dataframes
df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32'])

pd.merge(pd.merge(df1,df2,on='name'),df3,on='name')

alternatively, as mentioned by cwharland

df1.merge(df2,on='name').merge(df3,on='name')

回答 2

这是该join方法的理想情况

join方法正是针对这些类型的情况而构建的。您可以将任意数量的DataFrame与其一起加入。调用DataFrame与传递的DataFrames集合的索引连接。要使用多个DataFrame,必须将联接列放在索引中。

代码看起来像这样:

filenames = ['fn1', 'fn2', 'fn3', 'fn4',....]
dfs = [pd.read_csv(filename, index_col=index_col) for filename in filenames)]
dfs[0].join(dfs[1:])

使用@zero的数据,您可以执行以下操作:

df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32'])

dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:])

     attr11 attr12 attr21 attr22 attr31 attr32
name                                          
a         5      9      5     19     15     49
b         4     61     14     16      4     36
c        24      9      4      9     14      9

This is an ideal situation for the join method

The join method is built exactly for these types of situations. You can join any number of DataFrames together with it. The calling DataFrame joins with the index of the collection of passed DataFrames. To work with multiple DataFrames, you must put the joining columns in the index.

The code would look something like this:

filenames = ['fn1', 'fn2', 'fn3', 'fn4',....]
dfs = [pd.read_csv(filename, index_col=index_col) for filename in filenames)]
dfs[0].join(dfs[1:])

With @zero’s data, you could do this:

df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32'])

dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:])

     attr11 attr12 attr21 attr22 attr31 attr32
name                                          
a         5      9      5     19     15     49
b         4     61     14     16      4     36
c        24      9      4      9     14      9

回答 3

对于数据帧列表,也可以按以下步骤进行操作df_list

df = df_list[0]
for df_ in df_list[1:]:
    df = df.merge(df_, on='join_col_name')

或数据帧在生成器对象中(例如,以减少内存消耗):

df = next(df_list)
for df_ in df_list:
    df = df.merge(df_, on='join_col_name')

This can also be done as follows for a list of dataframes df_list:

df = df_list[0]
for df_ in df_list[1:]:
    df = df.merge(df_, on='join_col_name')

or if the dataframes are in a generator object (e.g. to reduce memory consumption):

df = next(df_list)
for df_ in df_list:
    df = df.merge(df_, on='join_col_name')

回答 4

python3.6.3和pandas0.22.0中concat,只要将要用于联接的列设置为索引,也可以使用

pd.concat(
    (iDF.set_index('name') for iDF in [df1, df2, df3]),
    axis=1, join='inner'
).reset_index()

其中df1df2df3定义为John Galt的答案

import pandas as pd
df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32']
)

In python 3.6.3 with pandas 0.22.0 you can also use concat as long as you set as index the columns you want to use for the joining

pd.concat(
    (iDF.set_index('name') for iDF in [df1, df2, df3]),
    axis=1, join='inner'
).reset_index()

where df1, df2, and df3 are defined as in John Galt’s answer

import pandas as pd
df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32']
)

回答 5

一个并不需要一个多指标进行连接操作。只需正确设置要在其上执行联接操作的索引列(df.set_index('Name')例如,该命令)

join默认情况下,该操作是对索引执行的。对于您的情况,只需要指定该Name列对应于您的索引即可。下面是一个例子

教程可能是有用的。

# Simple example where dataframes index are the name on which to perform the join operations
import pandas as pd
import numpy as np
name = ['Sophia' ,'Emma' ,'Isabella' ,'Olivia' ,'Ava' ,'Emily' ,'Abigail' ,'Mia']
df1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=name)
df2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'],         index=name)
df3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'],     index=name)
df = df1.join(df2)
df = df.join(df3)

# If you a 'Name' column that is not the index of your dataframe, one can set this column to be the index
# 1) Create a column 'Name' based on the previous index
df1['Name']=df1.index
# 1) Select the index from column 'Name'
df1=df1.set_index('Name')

# If indexes are different, one may have to play with parameter how
gf1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=range(8))
gf2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'], index=range(2,10))
gf3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'], index=range(4,12))

gf = gf1.join(gf2, how='outer')
gf = gf.join(gf3, how='outer')

One does not need a multiindex to perform join operations. One just need to set correctly the index column on which to perform the join operations (which command df.set_index('Name') for example)

The join operation is by default performed on index. In your case, you just have to specify that the Name column corresponds to your index. Below is an example

A tutorial may be useful.

# Simple example where dataframes index are the name on which to perform
# the join operations
import pandas as pd
import numpy as np
name = ['Sophia' ,'Emma' ,'Isabella' ,'Olivia' ,'Ava' ,'Emily' ,'Abigail' ,'Mia']
df1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=name)
df2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'],         index=name)
df3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'],     index=name)
df = df1.join(df2)
df = df.join(df3)

# If you have a 'Name' column that is not the index of your dataframe,
# one can set this column to be the index
# 1) Create a column 'Name' based on the previous index
df1['Name'] = df1.index
# 1) Select the index from column 'Name'
df1 = df1.set_index('Name')

# If indexes are different, one may have to play with parameter how
gf1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=range(8))
gf2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'], index=range(2,10))
gf3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'], index=range(4,12))

gf = gf1.join(gf2, how='outer')
gf = gf.join(gf3, how='outer')

回答 6

这是一种合并数据帧字典,同时使列名与字典同步的方法。如果需要,它还会填写缺失值:

这是合并数据帧字典的功能

def MergeDfDict(dfDict, onCols, how='outer', naFill=None):
  keys = dfDict.keys()
  for i in range(len(keys)):
    key = keys[i]
    df0 = dfDict[key]
    cols = list(df0.columns)
    valueCols = list(filter(lambda x: x not in (onCols), cols))
    df0 = df0[onCols + valueCols]
    df0.columns = onCols + [(s + '_' + key) for s in valueCols] 

    if (i == 0):
      outDf = df0
    else:
      outDf = pd.merge(outDf, df0, how=how, on=onCols)   

  if (naFill != None):
    outDf = outDf.fillna(naFill)

  return(outDf)

好的,让我们生成数据并进行测试:

def GenDf(size):
  df = pd.DataFrame({'categ1':np.random.choice(a=['a', 'b', 'c', 'd', 'e'], size=size, replace=True),
                      'categ2':np.random.choice(a=['A', 'B'], size=size, replace=True), 
                      'col1':np.random.uniform(low=0.0, high=100.0, size=size), 
                      'col2':np.random.uniform(low=0.0, high=100.0, size=size)
                      })
  df = df.sort_values(['categ2', 'categ1', 'col1', 'col2'])
  return(df)


size = 5
dfDict = {'US':GenDf(size), 'IN':GenDf(size), 'GER':GenDf(size)}   
MergeDfDict(dfDict=dfDict, onCols=['categ1', 'categ2'], how='outer', naFill=0)

Here is a method to merge a dictionary of data frames while keeping the column names in sync with the dictionary. Also it fills in missing values if needed:

This is the function to merge a dict of data frames

def MergeDfDict(dfDict, onCols, how='outer', naFill=None):
  keys = dfDict.keys()
  for i in range(len(keys)):
    key = keys[i]
    df0 = dfDict[key]
    cols = list(df0.columns)
    valueCols = list(filter(lambda x: x not in (onCols), cols))
    df0 = df0[onCols + valueCols]
    df0.columns = onCols + [(s + '_' + key) for s in valueCols] 

    if (i == 0):
      outDf = df0
    else:
      outDf = pd.merge(outDf, df0, how=how, on=onCols)   

  if (naFill != None):
    outDf = outDf.fillna(naFill)

  return(outDf)

OK, lets generates data and test this:

def GenDf(size):
  df = pd.DataFrame({'categ1':np.random.choice(a=['a', 'b', 'c', 'd', 'e'], size=size, replace=True),
                      'categ2':np.random.choice(a=['A', 'B'], size=size, replace=True), 
                      'col1':np.random.uniform(low=0.0, high=100.0, size=size), 
                      'col2':np.random.uniform(low=0.0, high=100.0, size=size)
                      })
  df = df.sort_values(['categ2', 'categ1', 'col1', 'col2'])
  return(df)


size = 5
dfDict = {'US':GenDf(size), 'IN':GenDf(size), 'GER':GenDf(size)}   
MergeDfDict(dfDict=dfDict, onCols=['categ1', 'categ2'], how='outer', naFill=0)

回答 7

简单的解决方案:

如果列名相似:

 df1.merge(df2,on='col_name').merge(df3,on='col_name')

如果列名不同:

df1.merge(df2,left_on='col_name1', right_on='col_name2').merge(df3,left_on='col_name1', right_on='col_name3').drop(columns=['col_name2', 'col_name3']).rename(columns={'col_name1':'col_name'})

Simple Solution:

If the column names are similar:

 df1.merge(df2,on='col_name').merge(df3,on='col_name')

If the column names are different:

df1.merge(df2,left_on='col_name1', right_on='col_name2').merge(df3,left_on='col_name1', right_on='col_name3').drop(columns=['col_name2', 'col_name3']).rename(columns={'col_name1':'col_name'})

回答 8

pandas文档中还有另一种解决方案(我在这里看不到),

使用 .append

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
   A  B
0  5  6
1  7  8
>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

ignore_index=True被用来忽略所附数据帧的索引,在源一个可用下一个索引代替。

如果列名不同,Nan将引入。

There is another solution from the pandas documentation (that I don’t see here),

using the .append

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
   A  B
0  5  6
1  7  8
>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The ignore_index=True is used to ignore the index of the appended dataframe, replacing it with the next index available in the source one.

If there are different column names, Nan will be introduced.


回答 9

这三个数据帧是

让我们使用嵌套的pd.merge合并这些框架

在这里,我们有了合并的数据框。

快乐的分析!!!

The three dataframes are

Let’s merge these frames using nested pd.merge

Here we go, we have our merged dataframe.

Happy Analysis!!!