Pandas: Combine many given series to create a DataFrame

Last update on May 01 2025 12:54:21 (UTC/GMT +8 hours)

72. Combine Many Series to Create a DataFrame

Write a Pandas program to combine many given series to create a DataFrame.

Sample Solution :

Python Code :

import pandas as pd
sr1 = pd.Series(['php', 'python', 'java', 'c#', 'c++'])
sr2 = pd.Series([1, 2, 3, 4, 5])
print("Original Series:")
print(sr1)
print(sr2)
print("Combine above series to a dataframe:")
ser_df = pd.DataFrame(sr1, sr2).reset_index()
print(ser_df.head())
print("\nUsing pandas concat:")
ser_df = pd.concat([sr1, sr2], axis = 1)
print(ser_df.head())
print("\nUsing pandas DataFrame with a dictionary, gives a specific name to the columns:")
ser_df = pd.DataFrame({"col1":sr1, "col2":sr2})
print(ser_df.head(5))

Sample Output:

Original Series:
0       php
1    python
2      java
3        c#
4       c++
dtype: object
0    1
1    2
2    3
3    4
4    5
dtype: int64
Combine above series to a dataframe:
   index       0
0      1  python
1      2    java
2      3      c#
3      4     c++
4      5     NaN

Using pandas concat:
        0  1
0     php  1
1  python  2
2    java  3
3      c#  4
4     c++  5

Using pandas DataFrame with a dictionary, gives a specific name to the columns:
     col1  col2
0     php     1
1  python     2
2    java     3
3      c#     4
4     c++     5

For more Practice: Solve these Related Problems:

• Write a Pandas program to combine three or more Series with different indices into a single DataFrame and then align them using outer join.
• Write a Pandas program to merge multiple Series into a DataFrame and then fill missing values resulting from non-matching indices.
• Write a Pandas program to concatenate several Series vertically into a DataFrame and then set a custom index for the result.
• Write a Pandas program to combine a list of Series into a DataFrame using a dictionary comprehension to assign column names.

Go to:

• Pandas DataFrame Exercises Home ↩
• Pandas Exercises Home ↩

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Next: Write a Pandas program to create DataFrames that contains random values, contains missing values, contains datetime values and contains mixed values.

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