Combine two Pandas series into a DataFrame

In this post, we will learn how to combine two series into a DataFrame? Before starting let’s see what a series is?
Pandas Series is a one-dimensional labeled array capable of holding any data type. In other terms, Pandas Series is nothing but a column in an excel sheet.

There are several ways to concatenate two series in pandas. Following are some of the ways:

Method 1: Using pandas.concat().

This method does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.

Code:

python

# import pandas library
import pandas as pd

# this user defines function
# creates a series
# from the passed list.
def createSeries (series_list):

  # create a series
  series_list = pd.Series(series_list)

  return series_list

# create a series of students
students = createSeries(['ABC', 'DEF',
                         'GHI', 'JKL',
                         'MNO', 'PQR'])
# create a series of subjects
subject = createSeries(['C++', 'C#',
                        'RUBY', 'SWIFT',
                        'GO', 'PYTHON'])
# create a series of marks
marks = createSeries([90, 30,
                      50, 70,
                      80, 60])
# create a dictionary
data = {"students": students,
        "subject": subject,
        "marks": marks}

# Concatenating the series side
# by side as depicted by axis=1
# If you want to concatenate the
# series one below the other
# change the axis to zero.
df = pd.concat(data,
               axis = 1)

# show the dataframe
df

Output:

Method 2: Using Series.append().

This method is a shortcut to concat. This method concatenates along axis=0 i.e. rows. Series.append() can take multiple objects to concatenate.

Code:

Python3

# import pandas library
import pandas as pd

# create a series
a = pd.Series(["ABC", "DEF",
               "GHI"])

# create a series
b = pd.Series(["JKL", "MNO",
               "PQR"])

# combine two series then
# create a dataframe
df = pd.DataFrame(a.append(b,
                  ignore_index = True))
# show the dataframe
df

Output: 

Method 3: Using pandas.merge().

Pandas have high performance in-memory join operations which is very similar to RDBMS like SQL. merge can be used for all database join operations  between dataframe or named series objects. You have to pass an extra parameter “name” to the series in this case.

Code:

Python3

# import pandas library
import pandas as pd

# create a series
a = pd.Series(["C++", "JAVA",
               "PYTHON", "DBMS",
               "C#"], name = "subjects")

# create a series
b = pd.Series(["30", "60",
               "90", "56",
               "50"], name = "marks")

# merge both series
df = pd.merge(a, b, right_index = True,
               left_index = True)
# show the dataframe
df

Output: 

Method 4: Using Dataframe.join().

This method can be used also to join two series but you have to convert one series into dataframe.

Code:

Python3

# import pandas library
import pandas as pd

# create a series
a = pd.Series(["C++", "JAVA",
               "PYTHON", "DBMS",
               "C#"], name = "subjects")

# create a series
b = pd.Series(["30", "60",
               "90", "56",
               "50"], name = "marks")

# create a dataframe
a = pd.DataFrame(a)

# add series 'b'
# into dataframe 'a'
df = a.join(b)

# show the dataframe
df

Output: