将一个pandas数据帧列表合并到一个pandas数据帧中

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

我有一个Pandas数据帧列表，我想将它组合成一个Pandas数据帧。我使用的是Python 2.7.10和Pandas 0.16.2

I created the list of dataframes from:

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)

This returns a list of dataframes

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

Here is some sample data

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]

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.

我想将d1，d2和d3组合成一个pandas数据帧。或者，在使用chunksize选项时，将大型表直接读入数据帧的方法将非常有用。

3 个解决方案

#1

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

鉴于所有数据帧都具有相同的列，您可以简单地连接它们：

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

#2

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

如果数据帧不是都具有相同的列，请尝试以下操作：

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

#3

You also can do it with functional programming:

您也可以使用函数式编程来完成它：

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

#1