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Creating a single DataFrame from multiple files in Pandas
schedule Mar 5, 2023
Last updated local_offer
Tags Python●Pandas
tocTable of Contents
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When files contain columns
Consider the following my_data_one.txt file:
A,B3,45,6
And my_data_two.txt file:
C,D10,1112,13
To read and combine the two files into a single DataFrame:
df_one = pd.read_csv("my_data_one.txt")df_two = pd.read_csv("my_data_two.txt")pd.concat([df_one, df_two], axis=1)
A B C D0 3 4 10 111 5 6 12 13
Here, axis=1 indicates that we want to concatenate the DataFrames horizontally, as opposed to vertically.
When files contain rows
Consider the following my_data_one.txt file:
A,B3,45,6
And my_data_two.txt:
A,B10,1112,13
To construct a single DataFrame from the two files:
df_one = pd.read_csv("my_data_one.txt")df_two = pd.read_csv("my_data_two.txt")df = pd.concat([df_one, df_two], axis=0, ignore_index=True)df
A B0 3 41 5 62 10 113 12 13
Note the following:
axis=0forconcat(~)means that we want to stack the DataFrames vertically, as opposed to horizontally.ignore_index=Trueforconcat(~)resets the index of the resulting DataFrame to the default integer indices ([0,1,2,3]). Without this parameter, we would end up with duplicate index values ([0,1,0,1]),
Related
Pandas | read_csv method
Reads a file, and parses its content into a DataFrame.
Pandas | concat method
Concatenates a list of Series or DataFrame, either horizontally or vertically.
Published by Isshin Inada
Edited by 0 others
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