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将熊猫数据框列表连接在一起

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问题：将熊猫数据框列表连接在一起
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回答 1
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回答 3

问题：将熊猫数据框列表连接在一起

我有一个熊猫数据框列表，我想将其合并为一个熊猫数据框。我正在使用Python 2.7.10和Pandas 0.16.2

我从以下位置创建了数据框列表：

import pandas as pd
dfs = []
sqlall = "select * from mytable"

for chunk in pd.read_sql_query(sqlall , cnxn, chunksize=10000):
    dfs.append(chunk)

这将返回数据帧列表

type(dfs[0])
Out[6]: pandas.core.frame.DataFrame

type(dfs)
Out[7]: list

len(dfs)
Out[8]: 408

这是一些示例数据

# sample dataframes
d1 = pd.DataFrame({'one' : [1., 2., 3., 4.], 'two' : [4., 3., 2., 1.]})
d2 = pd.DataFrame({'one' : [5., 6., 7., 8.], 'two' : [9., 10., 11., 12.]})
d3 = pd.DataFrame({'one' : [15., 16., 17., 18.], 'two' : [19., 10., 11., 12.]})

# list of dataframes
mydfs = [d1, d2, d3]

我想将d1，d2和组合d3成一个熊猫数据框。另外，使用该chunksize选项时将大表直接读入数据框的方法将非常有帮助。

点击查看英文原文

I have a list of Pandas dataframes that I would like to combine into one Pandas dataframe. I am using Python 2.7.10 and Pandas 0.16.2

I created the list of dataframes from:

import pandas as pd
dfs = []
sqlall = "select * from mytable"

for chunk in pd.read_sql_query(sqlall , cnxn, chunksize=10000):
    dfs.append(chunk)

This returns a list of dataframes

type(dfs[0])
Out[6]: pandas.core.frame.DataFrame

type(dfs)
Out[7]: list

len(dfs)
Out[8]: 408

Here is some sample data

# sample dataframes
d1 = pd.DataFrame({'one' : [1., 2., 3., 4.], 'two' : [4., 3., 2., 1.]})
d2 = pd.DataFrame({'one' : [5., 6., 7., 8.], 'two' : [9., 10., 11., 12.]})
d3 = pd.DataFrame({'one' : [15., 16., 17., 18.], 'two' : [19., 10., 11., 12.]})

# list of dataframes
mydfs = [d1, d2, d3]

I would like to combine d1, d2, and d3 into one pandas dataframe. Alternatively, a method of reading a large-ish table directly into a dataframe when using the chunksize option would be very helpful.

回答 0

鉴于所有数据框都具有相同的列，您可以简单地将concat它们：

import pandas as pd
df = pd.concat(list_of_dataframes)
点击查看英文原文

Given that all the dataframes have the same columns, you can simply concat them:

import pandas as pd
df = pd.concat(list_of_dataframes)

回答 1

如果数据帧的所有列都不相同，请尝试以下操作：

df = pd.DataFrame.from_dict(map(dict,df_list))
点击查看英文原文

If the dataframes DO NOT all have the same columns try the following:

df = pd.DataFrame.from_dict(map(dict,df_list))

回答 2

您也可以使用函数式编程来做到这一点：

from functools import reduce
reduce(lambda df1, df2: df1.merge(df2, "outer"), mydfs)
点击查看英文原文

You also can do it with functional programming:

from functools import reduce
reduce(lambda df1, df2: df1.merge(df2, "outer"), mydfs)

回答 3

concat 对于使用“ loc”命令针对现有数据框提取的列表理解也可以很好地工作 

df = pd.read_csv('./data.csv') # ie; Dataframe pulled from csv file with a "userID" column

review_ids = ['1','2','3'] # ie; ID values to grab from DataFrame

# Gets rows in df where IDs match in the userID column and combines them 

dfa = pd.concat([df.loc[df['userID'] == x] for x in review_ids])
点击查看英文原文

concat also works nicely with a list comprehension pulled using the “loc” command against an existing dataframe 

df = pd.read_csv('./data.csv') # ie; Dataframe pulled from csv file with a "userID" column

review_ids = ['1','2','3'] # ie; ID values to grab from DataFrame

# Gets rows in df where IDs match in the userID column and combines them 

dfa = pd.concat([df.loc[df['userID'] == x] for x in review_ids])