Merge Two Pandas Series Into a DataFrame

Merge Two Pandas Series Into a DataFrame

  1. Merge Two Pandas Series Into a DataFrame Using the pandas.concat() Method
  2. Merge Two Pandas Series Into a DataFrame Using the pandas.merge() Method
  3. Merge Two Pandas Series Into a DataFrame Using the Series.append() Method
  4. Merge Two Pandas Series Into a DataFrame Using the DataFrame.join() Method
  5. Conclusion

Pandas is a very popular and open-source python library that offers various functionalities or methods to merge or combine two pandas series in a DataFrame. In pandas, a series is a single one-dimensional labeled array that can handle any data type such as integer, float, string, python objects, etc. In simple words, the pandas series is a column in an excel sheet. Series stores the data in sequential order.

This tutorial will teach us how to merge or combine two or multiple pandas series into a DataFrame.

There are several methods available to merge two or multiple pandas series into a DataFrame such as pandas.concat(), Series.append(), pandas.merge(), and DataFrame.join(). We will explain each method in brief detail with the help of some examples in this article.

Merge Two Pandas Series Into a DataFrame Using the pandas.concat() Method

The pandas.concat() method performs all concatenations operations along an axis (either row wise or column wise). We can merge two or more pandas objects or series along a particular axis to create a DataFrame. The concat() method takes various parameters.

In the following example, we will pass pandas series to merge and axis=1 as parameters. The axis=1 means the series will merge as a column instead of rows. If we use axis=0, it will append pandas series as a row.

Example Code:

import pandas as pd

# Create Series by assigning names
products = pd.Series(['Intel Dell Laptops', 'HP Laptops', 'Lenavo Laptops', 'Acer Laptops'], name='Products')
dollar_price = pd.Series([350, 300, 400, 250 ], name='Price')
percentage_sale  = pd.Series([83, 99, 84, 76],name='Sale')

# merge two pandas series using the pandas.concat() method
df=pd.concat([products,dollar_price,percentage_sale ],axis=1)
print(df)

Output:

             Products  Price  Sale
0  Intel Dell Laptops    350    83
1          HP Laptops    300    99
2      Lenavo Laptops    400    84
3        Acer Laptops    250    76

Merge Two Pandas Series Into a DataFrame Using the pandas.merge() Method

The pandas.merge() is used to merge the complex column-wise combinations of DataFrame similar to SQL join or a merge operation. The merge() method can perform all database join operations between the named series objects or DataFrame. We have to pass an extra parameter name to the series when using the pandas.merge() method.

See the following example.

Example Code:

import pandas as pd

# Create Series by assigning names
products = pd.Series(['Intel Dell Laptops', 'HP Laptops', 'Lenavo Laptops', 'Acer Laptops'], name='Products')
dollar_price = pd.Series([350, 300, 400, 250 ], name='Price')

# using pandas series merge()
df = pd.merge(products, dollar_price, right_index = True,
               left_index = True)
print(df)

Output:

             Products  Price
0  Intel Dell Laptops    350
1          HP Laptops    300
2      Lenavo Laptops    400
3        Acer Laptops    250

Merge Two Pandas Series Into a DataFrame Using the Series.append() Method

The Series.append() method is a shortcut to the concat() method. This method appends the series along axis=0 or rows. Using this method, we can create a DataFrame by appending the series to another series as a row instead of columns.

We used the series.append() method in our source code in the following way:

Example Code:

import pandas as pd
  
# Using Series.append()
technical=pd.Series(["Pandas","Python","Scala","Hadoop"])
non_technical=pd.Series(["SEO","Graphic design","Content writing", "Marketing"])

# using the appen() method merge series and create dataframe
df = pd.DataFrame(technical.append(non_technical, 
                  ignore_index = True),columns=['Skills'])
print(df)

Output:

           Skills
0           Pandas
1           Python
2            Scala
3           Hadoop
4              SEO
5   Graphic design
6  Content writing
7        Marketing

Merge Two Pandas Series Into a DataFrame Using the DataFrame.join() Method

Using the DataFrame.join() method, we can join two series. When we use this method, we have to convert one series into the DataFrame object. Then we will use the result to combine with another series.

In the following example, we have converted the first series into a DataFrame object. Then, we used this DataFrame to merge with another series.

Example Code:

import pandas as pd

# Create Series by assigning names
products = pd.Series(['Intel Dell Laptops', 'HP Laptops', 'Lenavo Laptops', 'Acer Laptops'], name='Products')
dollar_price = pd.Series([350, 300, 400, 250 ], name='Price')

# Merge series using DataFrame.join() method
df=pd.DataFrame(products).join(dollar_price)
print(df)

Output:

             Products  Price
0  Intel Dell Laptops    350
1          HP Laptops    300
2      Lenavo Laptops    400
3        Acer Laptops    250

Conclusion

We learned in this tutorial how to merge two Pandas series into a DataFrame by using the four different ways. Moreover, we explored how these four methods pandas.concat(), Series.append(), pandas.merge(), and DataFrame.join() that facilitate us to solve the Pandas merge series task.

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