To append Series to DataFrame in Pandas we have several options:

(1) Append Series to DataFrame by pd.concat

pd.concat([df, humidity.to_frame()], axis=1)

(2) Append Series with - append (will be deprecated)

df.append(humidity, ignore_index=True)

(3) Append the Series to DataFrame with assign

df['humidity'] = humidity

Setup

In the examples, we'll use the following setup, which consists of 1 DataFrame and 2 Series:

import pandas as pd
import matplotlib.pyplot as plt

data={'day': [1, 2, 3, 4, 5],
     'temp': [9, 8, 6, 13, 10]}

df = pd.DataFrame(data)

data is:

day temp
0 1 9
1 2 8
2 3 6
3 4 13
4 5 10

The series are:

ser_col = pd.Series(data=[0.89, 0.86, 0.54, 0.73, 0.45], name='humidity')

which contains data for a new column:

0    0.89
1    0.86
2    0.54
3    0.73
4    0.45
Name: humidity, dtype: float64

And a Series which contains data for a new row:

ser_row = pd.Series({'day': 6, 'temp': 15})

result:

day      6
temp    15
dtype: int64

1: Append Series to DataFrame - pd.concat as column

Let's start by appending Pandas Series to DataFrame by method pd.concat().

We will add the Series to the DataFrame as a new column - this is possible by using parameter - axis=1:

pd.concat([df, ser_col.to_frame()], axis=1)

This will append the Series as a new column to the DataFrame:

day temp humidity
0 1 9 0.89
1 2 8 0.86
2 3 6 0.54
3 4 13 0.73
4 5 10 0.45

More information for this method: pandas.concat

2: Append Series to DataFrame - pd.concat as row

Next, we'll add the Series as a new row to a DataFrame. The method concat can be used once again:

pd.concat([df, ser_row.to_frame().T], ignore_index=True)

We can ignore the index by using - ignore_index=True:

day temp
0 1 9
1 2 8
2 3 6
3 4 13
4 5 10
5 6 15

This is equivalent to axis=0

3. Add Series to DataFrame with append

Let's see how to use the method pd.append() which will be deprecated in future. But it's still working and can be useful in some cases.

Pro Tip 1
Method pd.append is deprecated since version 1.4.0: Use concat() instead.

Append Series as a row

To append Pandas Series as a new row to a DataFrame we can do:

df.append(ser_row,ignore_index=True)

The new DataFrame is:

day temp
0 1 9
1 2 8
2 3 6
3 4 13
4 5 10
5 6 15

More information for this method: pandas.DataFrame.append

4. Add Series to DataFrame as column

Finally, let's take a look at a solution with assigning a new Series as a column in Pandas DataFrame. We can simply assign the Series to the DataFrame:

df['humidity'] = ser_col

The result is the a new column:

day temp humidity
0 1 9 0.89
1 2 8 0.86
2 3 6 0.54
3 4 13 0.73
4 5 10 0.45
Pro Tip 1
Pandas Series can be considered as a DataFrame column - that's why we can add them to a DataFrame.

Conclusion

In this article, we looked at different solutions for appending/adding a Series to DataFrame in Pandas. We focused on the most popular solutions with pd.concat but also covered a few alternatives.

We saw how to add a Series as a column or a row.