How to combine multiple Series to create a DataFrame in Python using Pandas?

Python
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#1

A series is similar to a column in a dataframe, while a dataframe is an object that can hold multiple columns or multiple series objects. In other words, series objects are one-dimensional (index and values) while dataframes objects are multi-dimensional.

Since a dataframe is essentially made up of several series, there must be ways of combining, merging, or joining multiple series objects to create a single dataframe. For example, I have the following series objects and I want to make a dataframe out of them:

series1 = pd.Series([4, 7, 4])
series2 = pd.Series(['data', 'science', 'dojo'])
series3 = pd.Series([1.0, 4.0, 7.0])

Could anyone please provide me with different methods and techniques to achieve this? I would highly appreciate it, thank you!

1 Like
#2

You are right about how you defined the difference between a series and a dataframe and yes, there are methods of achieving what you explained. You can use the concat() function which can be used for concatenating or joining two or more Pandas objects along a particular axis. You can specify:

  1. axis = 0 or axis = index to concatenate objects row-wise.
  2. axis = 1 or axis = column to concatenate objects column-wise.

The sample code below concatenates 3 different series column-wise.

Note: After the series' are joined in the dataframe, they are named by themselves as 0, 1, and 2 respectively.

#3

@mubashir_rizvi, You are explaining correctly about series and dataframes. To achieve your task, I have one more method. You can use the rename() method, another powerful and flexible way to change the name of the index or the column labels. This method is applied to the index using the index attribute.

#4

Hi @mubashir_rizvi! A simple way of achieving what you want to do is by using the DataFrame() constructor, which creates a new dataframe object and can take various inputs, including lists, arrays, dictionaries, etc. In the code below, 3 series objects are passed enclosed in a list to this function to create a dataframe.

Note: Remember that with this method, the series are combined row-wise and not column-wise.

#5

Hi @mubashir_rizvi, another approach that can be used to create a Pandas DataFrame is by passing a dictionary to the DataFrame() constructor. This involves creating a dictionary where the keys represent the column names, and the values represent the data for each column in the form of a Pandas Series. By passing this dictionary to the constructor, a DataFrame will be created with the specified column names and data. This can be a convenient way to create a DataFrame when working with data in a dictionary format.

The biggest advantage of this technique is that it joins the series column-wise and you can rename each column as you like by specifying keys in the dictionary.