Resetting Index of a DataFrame Using Reset_Index() and Concat() Functions in Pandas
Are you a data analyst or programmer who works with large data sets in pandas? Do you find it challenging to manipulate and reorganize your data frames to make sense of the information you are working with?
Fortunately, pandas comes with built-in functions that make handling large amounts of data much easier. Resetting index is one such function.
In this article, we will explore the reset_index() and concat() functions in pandas, two powerful tools that allow you to easily reset your indices and rearrange your data frames. Whether you are adding new rows, deleting them, sorting, or merging data frames, these functions will make your life much easier.
Resetting Index of a DataFrame Using Reset_Index() Function
Syntax of Reset_Index() Function in Pandas
To reset the index of a pandas data frame, you need to use the reset_index() function. The syntax of the reset_index() function is as follows:
df.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill=”)
The function takes in several parameters, including level, drop, inplace, col_level, and col_fill, which we will explore in detail below.
Reset Index of a DataFrame using reset_index() Function
Case 1: When Rows are Inserted in the DataFrame
To illustrate this case, we will first create two data frames and concatenate them using the concat() function. Here is an example:
df1 = pd.DataFrame({‘A’: [‘A0’, ‘A1’, ‘A2’, ‘A3’],
‘B’: [‘B0’, ‘B1’, ‘B2’, ‘B3’],
‘C’: [‘C0’, ‘C1’, ‘C2’, ‘C3’],
‘D’: [‘D0’, ‘D1’, ‘D2’, ‘D3’]},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({‘A’: [‘A4’, ‘A5’, ‘A6’, ‘A7’],
‘B’: [‘B4’, ‘B5’, ‘B6’, ‘B7’],
‘C’: [‘C4’, ‘C5’, ‘C6’, ‘C7’],
‘D’: [‘D4’, ‘D5’, ‘D6’, ‘D7’]},
index=[4, 5, 6, 7])
df = pd.concat([df1, df2])
print(df)
The output of this code snippet should be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
Now, let’s add two new rows to this data frame:
new_rows = pd.DataFrame({‘A’: [‘A8’, ‘A9’], ‘B’: [‘B8’, ‘B9’], ‘C’: [‘C8’, ‘C9’], ‘D’: [‘D8’, ‘D9’]})
df = pd.concat([df, new_rows])
print(df)
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
0 A8 B8 C8 D8
1 A9 B9 C9 D9
As you can see, two new rows have been added to the data frame. However, the index has been disrupted, and it is no longer sequential.
To reset the index, we will use the reset_index() function. Here is how you can do it:
df = df.reset_index(drop=True)
print(df)
With the drop=True parameter, we are telling pandas to drop the old index column and create a new one. The new index column will start from 0 and increase sequentially.
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
8 A8 B8 C8 D8
9 A9 B9 C9 D9
Case 2: When Rows are Deleted in the DataFrame
In this case, we will delete two rows from the data frame. Here is how you can do it:
df = df.drop([8,9])
print(df)
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
As you can see, two rows have been deleted from the data frame, but the index is no longer sequential. We can reset the index using reset_index() function just as we did in the previous case.
Here is how you can do it:
df = df.reset_index(drop=True)
print(df)
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
Case 3: When Rows are Sorted in the DataFrame
In this case, we will sort the data frame by a specific column, and the resulting order will disrupt the index. Here is an example:
df = df.sort_values(‘A’)
print(df)
As you can see, the data frame has been sorted by column ‘A’, and the index is no longer sequential. We can reset the index using reset_index() function, just as we did in the previous cases.
Here is how you can do it:
df = df.reset_index(drop=True)
print(df)
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
Case 4: When Two Data Frames are Appended
In this case, we will create two separate data frames and append them using the concat() function. Here is an example:
df1 = pd.DataFrame({‘A’: [‘A0’, ‘A1’, ‘A2’, ‘A3’],
‘B’: [‘B0’, ‘B1’, ‘B2’, ‘B3’],
‘C’: [‘C0’, ‘C1’, ‘C2’, ‘C3’],
‘D’: [‘D0’, ‘D1’, ‘D2’, ‘D3’]})
df2 = pd.DataFrame({‘A’: [‘A4’, ‘A5’, ‘A6’, ‘A7’],
‘B’: [‘B4’, ‘B5’, ‘B6’, ‘B7’],
‘C’: [‘C4’, ‘C5’, ‘C6’, ‘C7’],
‘D’: [‘D4’, ‘D5’, ‘D6’, ‘D7’]})
df = pd.concat([df1, df2], ignore_index=True)
print(df)
The output of this code will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
As you can see, we created two data frames and then concatenated them. However, the index is no longer sequential.
We can reset the index using the reset_index() function. However, there is a simpler way to do it – we can use the ignore_index parameter in the concat() function.
Here is how you can do it:
df = pd.concat([df1, df2], ignore_index=True)
print(df)
The output of this code will be the same as above:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
Resetting Index of a DataFrame Using Concat() Function
The concat() function can be used not only to merge data frames but also to reset the index. By default, the concatenation of two data frames will carry on with their original indices.
However, if we want to reset the index, we can use the ignore_index parameter within the concat() function. Here is how you can do it:
df = pd.concat([df1, df2], ignore_index=True)
print(df)
In this case, we reset the index of the concatenated data frames using the ignore_index parameter. The resulting data frame has a sequential index ranging from 0 to the total number of rows in the data frame.
Conclusion
Resetting the index of a data frame in pandas is a necessary operation that allows you to manipulate and process your data better. The reset_index() and concat() functions in pandas simplify the process of resetting the index, regardless of the specific use case.
Applying either of these functions will make your programming experience faster and easier with cleaner data. In the analytical world, pandas is one of the most popular and widely used libraries that can help in analyzing large data sets.
The library is highly efficient, reliable and easy to use. It is a go-to tool for data scientists and programmers alike.
In this article, we looked at two functions, reset_index() and concat(), that can be used in pandas to reset the index of a DataFrame and how to use them with various use cases. Resetting the index of a DataFrame is an essential operation that helps in organizing and exploring data, which is critical for data analysis and data visualization processes.
In pandas, the reset_index() function helps in converting an index into a column or resetting an existing index. We learned that the function takes in several parameters like ‘level,’ ‘drop,”inplace’, ‘col_level’, and ‘col_fill’ used for specific working conditions.
We first looked at four situations where reset_index() function can be used to organize a DataFrame. Suppose we insert new rows into an existing DataFrame, delete rows from the existing dataset, sort the rows in the DataFrame and concatenate two DataFrames.
In that case, the necessary parameters and functions are used to reset the index. We learned to handle each of these situations in a detailed manner, by providing clearly illustrated examples to help an analyst understand how to use pandas to reset the index in a DataFrame.
In addition, we also explored the role of the concat() function, which provides an easy solution for appending two DataFrames while maintaining the sequence of the indices. The function has a default behavior of carrying on the index sequence.
However, we can instruct concat() to ignore existing indices and create a new series of sequential integers as the DataFrame’s index.
By using the ignore_index argument in the concat() function, we could reset the index of a DataFrame.
Also, we learned that using reset_index() and concat() functions could work together to achieve the desired output of a DataFrame with proper index sequence, regardless of the initial use case or DataFrame’s structure. In conclusion, using reset_index() and concat() functions in pandas is essential when dealing with DataFrames with non-sequential indices.
With these functions, we can efficiently analyze, manipulate, and visualize data while organizing and resting the index of the DataFrame accordingly. Furthermore, as illustrated with the various use cases, the reset_index() and concat() functions in pandas have a range of ways to help reset the index according to your desired use.
With the knowledge of these pandas functionalities, analysts can write better, more efficient scripts while analyzing large datasets with Python and pandas. In this article, we have explored two important pandas functions, reset_index() and concat(), that help reset the index of a DataFrame to organize and explore data effectively.
We learned how reset_index() can be used to reset the index of a pandas DataFrame and explored four use cases on how to handle non-sequential indices, such as inserting new rows, deleting rows, sorting rows and concatenating two DataFrames. We also discovered how to use the concat() function to append two DataFrames while maintaining the index sequence.
With the knowledge of these functions, analysts can write better, more efficient scripts while analyzing large data sets. These pandas functionalities emphasize the importance of organizing data to make effective use of it for data analysis and visualization purposes.