Pandas – Create DataFrame From Multiple Series

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  • Post category:Pandas
  • Post last modified:October 5, 2023

If you have a multiple series and wanted to create a pandas DataFrame by appending each series as a columns to DataFrame, you can use concat() method.

In pandas, Series is a one-dimensional labeled array capable of holding any data type(integers, strings, floating-point numbers, Python objects, etc.). Series stores data in sequential order. It is one-column information similar to a columns in an excel sheet/SQL table.

When you combine multiple pandas Series into a DataFrame, it creates a DataFrame with the number of columns equivalent to number of series you are merging.

1. Create pandas DataFrame From Multiple Series

You can create a DataFrame from multiple Series objects by adding each series as a columns.

By using concat() method you can merge multiple series together into DataFrame. This takes several params, for our scenario we use list that takes series to combine and axis=1 to specify merge series as columns instead of rows. Note that using axis=0 appends series to rows instead of columns.

import pandas as pd
# Create pandas Series
courses = pd.Series(["Spark","PySpark","Hadoop"])
fees = pd.Series([22000,25000,23000])
discount  = pd.Series([1000,2300,1000])

# Combine two series.

# It also supports to combine multiple series.

Yields below output.

# Output:
         0      1     2
0    Spark  22000  1000
1  PySpark  25000  2300
2   Hadoop  23000  1000

It assigns numbers to columns. you can assign names to Series to use it as columns.

# Create Series by assigning names
courses = pd.Series(["Spark","PySpark","Hadoop"], name='courses')
fees = pd.Series([22000,25000,23000], name='fees')
discount  = pd.Series([1000,2300,1000],name='discount')


Yields below output.

# Output:
   courses   fees  discount
0    Spark  22000      1000
1  PySpark  25000      2300
2   Hadoop  23000      1000

Let’s see how to assign an index to Series and provide custom column names to the DataFrame.

# Assign Index to Series
courses.index = index_labels
fees.index = index_labels
discount.index = index_labels

# Concat Series by Changing Names
df=pd.concat({'Courses': courses,
              'Course_Fee': fees,
              'Course_Discount': discount},axis=1)

Yields below output.

# Output:
    Courses  Course_Fee  Course_Discount
r1    Spark       22000             1000
r2  PySpark       25000             2300
r3   Hadoop       23000             1000

Finally, let’s see how to rest the index using reset_index() method. This moves the current index as a column and adds a new index to a combined DataFrame.

# Change the index to a column & create new index
df = df.reset_index()

Yields below output.

# Output:
  index  Courses  Course_Fee  Course_Discount
0    r1    Spark       22000             1000
1    r2  PySpark       25000             2300
2    r3   Hadoop       23000             1000

Happy Learning !!


In this article you have learned how to create a DataFrame from multiple pandas Series objects. On DataFrame each series becomes a column. Also learned how to change the column names while creating a DataFrame and reset indexes.

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I am a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, I have honed my expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. My journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. I have started this to share my experiences with the data as I come across. You can learn more about me at LinkedIn

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