Adventures in Machine Learning

Streamline Your Data Analysis: How to Add Columns to Pandas DataFrame

Adding a new column to a Pandas DataFrame can be a crucial step when analyzing data. It allows you to introduce new variables, index new parameters, and better organize your data.

Whether you need to add a column to the beginning, the middle, or the end of your DataFrame, Pandas has a simple and flexible method to get the job done right. In this article, we will explain how you can add a new column to a Pandas DataFrame, with a focus on inserting a new column at the first, middle, and last positions.

Inserting a New Column as the First Column

To insert a new column as the first column in a Pandas DataFrame, there are a few steps you need to follow. First, you need to create your DataFrame with the necessary data you want.

For example, let’s create a basic DataFrame with three columns: ‘points’, ‘assists’, and ‘rebounds.’

“`

import pandas as pd

df = pd.DataFrame({‘points’: [10, 20, 30, 40, 50],

‘assists’: [5, 10, 15, 20, 25],

‘rebounds’: [2, 4, 6, 8, 10]})

print(df)

“`

Output:

“`

points assists rebounds

0 10 5 2

1 20 10 4

2 30 15 6

3 40 20 8

4 50 25 10

“`

Now that we have created our DataFrame, we can insert our new column to the front. In this example, we will add a new column named ‘player’ to the front of our DataFrame.

“`

# Creating player values for our new column

player_vals = [‘Player A’, ‘Player B’, ‘Player C’, ‘Player D’, ‘Player E’]

# Inserting the new column named ‘player’ at position 0 of our DataFrame

df.insert(loc=0, column=’player’, value=player_vals, allow_duplicates=False)

print(df)

“`

Output:

“`

player points assists rebounds

0 Player A 10 5 2

1 Player B 20 10 4

2 Player C 30 15 6

3 Player D 40 20 8

4 Player E 50 25 10

“`

The first parameter `loc` specifies the location where the new column needs to be inserted, in this case, `0` indicates the first position. The second parameter `column` specifies the name of the new column, and the third parameter `value` specifies the values to be inserted.

Finally, the parameter `allow_duplicates=False` ensures that we don’t insert any duplicate column names.

Inserting a New Column as a Middle Column

Inserting a new column in the middle of a DataFrame is similar to adding a new column at the beginning. The only difference is that you need to specify the location of the new column.

For instance, let’s assume that we want to add a new column named ‘salary’ after the ‘assists’ column. First, we need to identify the column position of assists.

Since we have three columns, points(0), assists(1), and rebounds(2), then ‘assists’ is the second column (position 1). “`

# Creating salary values

salary_vals = [120000, 150000, 110000, 130000, 140000]

# Inserting the new column named ‘salary’ at position 1 of our DataFrame

df.insert(loc=1, column=’salary’, value=salary_vals, allow_duplicates=False)

print(df)

“`

Output:

“`

player salary points assists rebounds

0 Player A 120000 10 5 2

1 Player B 150000 20 10 4

2 Player C 110000 30 15 6

3 Player D 130000 40 20 8

4 Player E 140000 50 25 10

“`

The parameter `loc=1` specifies that our new column (the salary column) should be inserted at position 1, i.e., after the first column (player column).

Inserting a New Column as the Last Column

To add a new column at the end of a Pandas DataFrame, you need to specify the location as the length of the DataFrame. The last column index in a Pandas DataFrame is always `len(df.columns) – 1`.

“`

# Creating age values for our new column

age_vals = [25, 23, 26, 24, 22]

# Inserting the new column named ‘age’ at the last position of our DataFrame

df.insert(loc=len(df.columns), column=’age’, value=age_vals, allow_duplicates=False)

print(df)

“`

Output:

“`

player salary points assists rebounds age

0 Player A 120000 10 5 2 25

1 Player B 150000 20 10 4 23

2 Player C 110000 30 15 6 26

3 Player D 130000 40 20 8 24

4 Player E 140000 50 25 10 22

“`

The last parameter, `len(df.columns)`, specifies the location of our new column (the `age` column), which in this case, is inserted after the last column (rebounds).

Conclusion

Adding a new column to a Pandas DataFrame provides many benefits to the users, including better organization and indexing of data. In this article, we explained how to add a new column to the beginning, middle, and end of a Pandas DataFrame.

We hope this tutorial has been informative and beneficial to you. Example 2: Insert New Column as a Middle Column

Adding a new column as a middle column in a Pandas DataFrame requires a slightly different approach compared to adding a new column as the first or the last column.

In this example, we will demonstrate how to add a new column as the third column in the existing Pandas DataFrame. “`

# Creating Pandas DataFrame with existing columns

df = pd.DataFrame({‘points’: [10, 20, 30, 40, 50],

‘assists’: [5, 10, 15, 20, 25],

‘rebounds’: [2, 4, 6, 8, 10]})

print(df)

“`

Output:

“`

points assists rebounds

0 10 5 2

1 20 10 4

2 30 15 6

3 40 20 8

4 50 25 10

“`

The above code creates a Pandas DataFrame with three columns: ‘points’, ‘assists’, and ‘rebounds’. Our goal is to add a new column ‘player’ to the DataFrame as the third column.

“`

# Creating player values for our new column

player_vals = [‘Player A’, ‘Player B’, ‘Player C’, ‘Player D’, ‘Player E’]

# Inserting the new column named ‘player’ at position 2 of our DataFrame

df.insert(loc=2, column=’player’, value=player_vals, allow_duplicates=False)

print(df)

“`

Output:

“`

points assists player rebounds

0 10 5 Player A 2

1 20 10 Player B 4

2 30 15 Player C 6

3 40 20 Player D 8

4 50 25 Player E 10

“`

The above code created a new column named ‘player’ and inserted it at position 2 of the DataFrame. The third parameter, `column=player`, specifies the name of the new column, and the fourth parameter, `value=player_vals`, specifies the values of the new column.

The parameter `loc=2` specifies the location for the new column, which is the third position in the DataFrame. Example 3: Insert New Column as Last Column

Inserting a new column at the end of a Pandas DataFrame is straightforward.

In this example, we will add a new column named ‘team’ to the end of the DataFrame. “`

# Creating Pandas DataFrame with existing columns

df = pd.DataFrame({‘points’: [10, 20, 30, 40, 50],

‘assists’: [5, 10, 15, 20, 25],

‘rebounds’: [2, 4, 6, 8, 10]})

print(df)

“`

Output:

“`

points assists rebounds

0 10 5 2

1 20 10 4

2 30 15 6

3 40 20 8

4 50 25 10

“`

The above code creates a Pandas DataFrame with three columns: ‘points’, ‘assists’, and ‘rebounds’. Our goal is to add a new column ‘team’ to the DataFrame as the last column.

“`

# Creating team values for our new column

team_vals = [‘A’, ‘B’, ‘C’, ‘D’, ‘E’]

# Inserting the new column named ‘team’ at the end of the DataFrame

df[‘team’] = team_vals

print(df)

“`

Output:

“`

points assists rebounds team

0 10 5 2 A

1 20 10 4 B

2 30 15 6 C

3 40 20 8 D

4 50 25 10 E

“`

The above code effectively created a new column named ‘team’ and appended it to the end of the DataFrame by using the indexing operator `[]`. By specifying the new column name within square brackets and assigning values to it, the DataFrame automatically creates a new column.

Conclusion

Adding a new column to a Pandas DataFrame is a simple and powerful method when it comes to analyzing complex data structures. Whether you need to add a new column at the beginning, the middle, or the end of your DataFrame, Pandas’ flexible method helps you get the job done with ease.

With the examples provided in this article, you should be able to insert a new column effortlessly and effectively in your future data analysis projects. In conclusion, adding a new column to a Pandas DataFrame is an essential aspect of data analysis because it provides robust organization and indexing of data.

Whether you need to insert a new column at the beginning, middle, or end of your DataFrame, Pandas’ flexible method enables you to do so easily. By following the example provided in this article, you can insert a new column effortlessly and effectively in your future data analysis projects.

The ability to add a new column is just one of the many powerful features of Pandas that make it a popular tool amongst data scientists and analysts. By mastering the art of adding a new column, you can elevate your proficiency in data analysis and obtain useful insights from your data.

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