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# Creating a single DataFrame from multiple files in Pandas

schedule Aug 10, 2023
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local_offer
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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=0` for `concat(~)` means that we want to stack the DataFrames vertically, as opposed to horizontally.

• `ignore_index=True` for `concat(~)` 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]`),

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