Let’s say you already have a pandas DataFrame with few columns and you would like to add/merge Series as columns into existing DataFrame, this is certainly possible using pandas.Dataframe.merge() method.
I will explain with the examples in this article. first create a sample DataFrame and a few Series. You can also try by combining Multiple Series to create DataFrame.
import pandas as pd
technologies = ({
'Courses':["Spark","PySpark","Hadoop"],
'Fee' :[22000,25000,23000]
})
df = pd.DataFrame(technologies)
print(df)
# Create Series
discount = pd.Series([1000,2300,1000], name='Discount')
1. Merge Series into pandas DataFrame
Now let’s say you wanted to merge by adding Series object discount to DataFrame df.
# Merge Series into DataFrame
df2=df.merge(discount,left_index=True, right_index=True)
print(df2)
Yields below output. It merges the Series with DataFrame on index.
Courses Fee Discount
0 Spark 22000 1000
1 PySpark 25000 2300
2 Hadoop 23000 1000
This also works if your rows are in different order, but in this case you should have custom indexes. I will leave this to you to explore.
# Rename Series before Merge
df2=df.merge(discount.rename('Course_Discount'),
left_index=True, right_index=True)
print(df2)
Yields below output
Courses Fee Course_Discount
0 Spark 22000 1000
1 PySpark 25000 2300
2 Hadoop 23000 1000
2. Using Series.to_frame() & DataFrame.merge() Methods
You can also create a DataFrame from Series using Series.to_frame() and use it with DataFrame to merge.
# Merge by creating DataFrame from Series
df2=df.merge(discount.to_frame(), left_index=True, right_index=True)
print(df2)
Yields same output as in first example.
Conclusion
In this article I have explained how to merge/add series objects to existing pandas DataFrame as columns by using merge() method. also covered by creating a DataFrame from series using to_frame() and using on merge() method.
Happy Learning
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