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# Combining multiple Series into a DataFrame in Pandas

schedule Mar 5, 2023
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To combine multiple Series into a single DataFrame, use the `concat(~)` method or the `DataFrame(~)` constructor.

# Combining Series horizontally

## Series represent columns

To combine two Series horizontally:

``` s1 = pd.Series([3,4], index=["a","b"])   # represents a columns2 = pd.Series([5,6], index=["a","b"])pd.concat([s1,s2], axis=1) 0 1a 3 5b 4 6 ```

Note the following:

• each Series represents a column

• the parameter `axis=1` for `concat(~)` is used to perform horizontal concatenation, as opposed to vertical.

Note that if your Series do not have exact matching index, the resulting DataFrame will have `NaN` values:

``` s1 = pd.Series([3,4], index=["a","b"])s2 = pd.Series([5,6], index=["b","c"])pd.concat([s1,s2], axis=1) 0 1a 3.0 NaNb 4.0 5.0c NaN 6.0 ```

## Series represent rows

To combine two Series where each series represents a row:

``` s1 = pd.Series([3,4], index=["a","b"])   # represents a rows2 = pd.Series([5,6], index=["a","b"])pd.DataFrame([s1,s2]) a b0 3 41 5 6 ```

Note that if your Series do not have exact matching index, the resulting DataFrame will have `NaN` values:

``` s1 = pd.Series([3,4], index=["a","b"])s2 = pd.Series([5,6], index=["b","c"])pd.DataFrame([s1,s2])    a    b    c0  3.0  4.0  NaN1  NaN  5.0  6.0 ```

# Combining Series vertically

To combine two Series vertically to form a DataFrame:

``` s1 = pd.Series([3,4], index=["a","b"])s2 = pd.Series([5,6], index=["a","b"])pd.DataFrame(pd.concat([s1,s2])) 0a 3b 4a 5b 6 ```

Note that calling `concat(~)` on two series with the default `axis=0` results in a Series, and so we need to convert this Series into a DataFrame via the DataFrame constructor.

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