将熊猫数据框列表连接在一起

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.

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

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

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

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

You also can do it with functional programming:

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

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])