Adding and subtracting columns are common tasks when working with data using pandas DataFrame. In this article, we will discuss the syntax for subtracting one column from another in pandas DataFrame, handling missing values when subtracting columns, and creating a pandas DataFrame.
Subtracting Columns in Pandas DataFrame
Subtracting one column from another in pandas DataFrame involves using the minus(-) operator. Here is the syntax for subtracting one column from another in pandas DataFrame:
df['New Column'] = df['Column1'] - df['Column2']In the above syntax, df is the DataFrame, New Column is the new column to be created with the result of the subtraction, Column1 and Column2 are the columns to be subtracted.
Example 1: Subtracting Two Columns and Assigning the Result to a New Column
Let’s illustrate subtracting two columns and assigning the result to a new column using an example.
import pandas as pd
# Create a pandas DataFrame
data = {'Column1': [7, 6, 4, 3, 1], 'Column2': [3, 5, 7, 2, 10]}
df = pd.DataFrame(data)
# Subtract Column2 from Column1 and assign the result to a new column
df['New Column'] = df['Column1'] - df['Column2']
# Show the resulting DataFrame
print(df)Output:
Column1 Column2 New Column
0 7 3 4
1 6 5 1
2 4 7 -3
3 3 2 1
4 1 10 -9In the above example, we created a pandas DataFrame with two columns, Column1 and Column2. We then subtracted Column2 from Column1 and assigned the result to a new column called New Column.
The resulting DataFrame shows the three columns (Column1, Column2, and New Column).
Example 2: Handling Missing Values When Subtracting Columns
Sometimes when subtracting columns in pandas DataFrame, missing values might be present in one or both of the columns.
Result of Subtraction When Missing Values Exist
If there is a missing value in one of the columns, the result will be a missing value (NaN). Here is an example:
import pandas as pd
# Create a pandas DataFrame with missing values
data = {'Column1': [7, 6, 4, 3, 1, pd.NA], 'Column2': [3, 5, 7, pd.NA, 2, 10]}
df = pd.DataFrame(data)
# Subtract Column2 from Column1 and assign the result to a new column
df['New Column'] = df['Column1'] - df['Column2']
# Show the resulting DataFrame
print(df)Output:
Column1 Column2 New Column
0 7 3.0 4.0
1 6 5.0 1.0
2 4 7.0 -3.0
3 3 NaN NaN
4 1 2.0 -1.0
5 10.0 In the above example, we created a pandas DataFrame with missing values in both of the columns. When we subtracted Column2 from Column1, the resulting column has missing values.
Replacing Missing Values with Zeros Before Subtraction
If you want the missing values to be treated as zeros in the subtraction operation, you can use the fillna() method to replace the missing values with zeros. Here is an example:
import pandas as pd
# Create a pandas DataFrame with missing values
data = {'Column1': [7, 6, 4, 3, 1, pd.NA], 'Column2': [3, 5, 7, pd.NA, 2, 10]}
df = pd.DataFrame(data)
# Fill missing values with zeros
df.fillna(0, inplace=True)
# Subtract Column2 from Column1 and assign the result to a new column
df['New Column'] = df['Column1'] - df['Column2']
# Show the resulting DataFrame
print(df)Output:
Column1 Column2 New Column
0 7 3.0 4.0
1 6 5.0 1.0
2 4 7.0 -3.0
3 3 0.0 3.0
4 1 2.0 -1.0
5 0 10.0 -10.0In the above example, we used the fillna() method to replace the missing values with zeros. When we subtracted Column2 from Column1, the resulting column has the values computed as if the missing values were zeros.
Creating a Pandas DataFrame
Creating a pandas DataFrame involves using the pd.DataFrame() function. Here is the syntax for creating a DataFrame:
import pandas as pd
# Create a pandas DataFrame
df = pd.DataFrame({'Column1': [value1, value2, value3, ...], 'Column2': [value1, value2, value3, ...]})In the above syntax, df is the name of the DataFrame, Column1 and Column2 are the column names, value1, value2, value3, … are the values for each column.
Example of Creating a DataFrame with Specified Columns and Values
Let’s illustrate creating a pandas DataFrame with specified columns and values using an example.
import pandas as pd
# Create a pandas DataFrame with specified columns and values
df = pd.DataFrame({'Name': ['John', 'Mary', 'Mark', 'Jessica'],
'Age': [25, 32, 18, 43],
'Country': ['USA', 'Canada', 'Australia', 'UK']})
# Show the resulting DataFrame
print(df)Output:
Name Age Country
0 John 25 USA
1 Mary 32 Canada
2 Mark 18 Australia
3 Jessica 43 UKIn the above example, we created a pandas DataFrame with three columns (Name, Age, and Country). Each column has specific values specified in a dictionary passed to the pd.DataFrame() function.
Conclusion
In this article, we have discussed the syntax for subtracting one column from another in pandas DataFrame, handling missing values when subtracting columns and creating a pandas DataFrame. We hope you find the information presented here helpful in your data analysis projects.
Remember to always practice and experiment with the code provided in this article to enhance your understanding.
3) Viewing Pandas DataFrame
Pandas DataFrame is a 2-dimensional data structure that allows us to work with data in Python. When working with pandas DataFrame, it is essential to know how to view the contents of the DataFrame.
Syntax for Viewing a DataFrame
The primary method for viewing pandas DataFrame is by using the print() function. Here is the syntax for viewing a DataFrame:
print(dataframe)In the above syntax, dataframe is the DataFrame that you want to view.
Example of Viewing a DataFrame in Various Formats
Here are some examples of how to view pandas DataFrame in various formats:
1. Viewing all rows and columns of a DataFrame
You can view all rows and columns of a DataFrame using the pd.set_option() function.
Here is an example:
import pandas as pd
# Create a pandas DataFrame
data = {'Name': ['John', 'Mary', 'Mark', 'Jessica'], 'Age': [25, 32, 18, 43],
'Country': ['USA', 'Canada', 'Australia', 'UK']}
df = pd.DataFrame(data)
# Set option to view all rows and columns
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# View the DataFrame
print(df)Output:
Name Age Country
0 John 25 USA
1 Mary 32 Canada
2 Mark 18 Australia
3 Jessica 43 UKIn the above example, we created a pandas DataFrame with three columns (‘Name’, ‘Age’, and ‘Country’). We used the pd.set_option() function to set the option to view all rows and columns.
2. Viewing a Selected Number of Rows and Columns
You can view a selected number of rows and columns of a DataFrame by using the iloc[] method.
Here is an example:
import pandas as pd
# Create a pandas DataFrame
data = {'Name': ['John', 'Mary', 'Mark', 'Jessica'], 'Age': [25, 32, 18, 43],
'Country': ['USA', 'Canada', 'Australia', 'UK']}
df = pd.DataFrame(data)
# View the first two rows and first two columns of the DataFrame
print(df.iloc[:2, :2])Output:
Name Age
0 John 25
1 Mary 32In the above example, we used the iloc[] method to select the first two rows and the first two columns of the DataFrame.
4) Importing Data to Pandas DataFrame
Pandas DataFrame is a powerful data structure that allows us to work with data in Python. To work with pandas DataFrame, we often need to import data from various sources.
Syntax for Importing Data to a DataFrame
To import data to pandas DataFrame, we use various methods provided by pandas. Here is the syntax for importing data to a DataFrame:
import pandas as pd
# Import data to pandas DataFrame
df = pd.() In the above syntax, df is the DataFrame where we will store the imported data,
Example of Importing Data from a CSV File
CSV (Comma-Separated Values) is a commonly used file format to store the data in tabular form. Here is an example of how to import data from a CSV file:
import pandas as pd
# Import data from a CSV file
df = pd.read_csv('data.csv')
# View the resulting DataFrame
print(df.head())In the above example, we used the read_csv() method from pandas to import data from a CSV file. We stored the imported data in the DataFrame called df.
Finally, we used the head() method to view the first five rows of the DataFrame.
Conclusion
In this addition to the article, we have discussed how to view pandas DataFrame using various methods, and how to import data from different sources to pandas DataFrame. We hope this information will be helpful in your data analysis projects and will help you get started with pandas DataFrame efficiently.
Always remember to practice and experiment with the code to enhance your understanding.
5) Filtering Rows in Pandas DataFrame
Filtering rows based on certain conditions in pandas DataFrame is a common task. This allows us to extract the necessary data from a dataset and work with a subset of data, which is often relevant to our research questions.
Syntax for Filtering Rows in a DataFrame by a Certain Condition
To filter rows based on a certain condition, we use the loc[] method in pandas, which allows us to select rows that satisfy a particular condition. Here’s the syntax:
import pandas as pd
# Filter rows based on condition
df_filtered = df.loc[condition]In the above syntax, df_filtered is the new DataFrame that will store the filtered rows, df is the initial DataFrame, and condition is the condition that is used to filter the rows.
Example of Filtering Rows Based on a Condition
Let’s see an example of filtering rows based on a condition:
import pandas as pd
# Create a pandas DataFrame
data = {'Person': ['John', 'Mary', 'Mark', 'Jessica'], 'Age': [25, 32, 18, 43],
'Country': ['USA', 'Canada', 'Australia', 'UK']}
df = pd.DataFrame(data)
# Filter rows where Age is greater than 30
df_filtered = df.loc[df['Age'] > 30]
# View the resulting filtered DataFrame
print(df_filtered)Output:
Person Age Country
1 Mary 32 Canada
3 Jessica 43 UKIn the above example, we created a pandas DataFrame with three columns (‘Person’, ‘Age’, and ‘Country’). We then filtered the rows where the Age column is greater than 30.
We stored the filtered rows in a new DataFrame called df_filtered and then printed the resulting DataFrame.
6) Grouping Data in Pandas DataFrame
Grouping data in pandas DataFrame is a common task when working with datasets. Grouping data involves splitting the data into groups based on specific criteria, such as values in a specific column, and then applying a function to each group.
Syntax for Grouping Data in a DataFrame
To group data in pandas DataFrame, we use the groupby() function, which groups rows based on the values in a specific column. Here’s the syntax:
import pandas as pd
# Group data in DataFrame by one or more columns
grouped_data = df.groupby(['Column1', 'Column2',...])In the above syntax, grouped_data is the new DataFrame that stores the grouped data, df is the initial DataFrame, and Column1, Column2, and so on are the columns based on which the data is grouped.
Example of Grouping Data and Applying a Function
Let’s see an example of grouping data and applying a function:
import pandas as pd
# Create a pandas DataFrame
data = {'Name': ['John', 'John', 'Mary', 'Mary', 'Mark', 'Mark'],
'Age': [25, 32, 18, 43, 19, 22],
'Country': ['USA', 'USA', 'Canada', 'Canada', 'Australia', 'Australia']}
df = pd.DataFrame(data)
# Group the data by 'Country' and calculate the average age per country
grouped_data = df.groupby(['Country'])['Age'].mean()
# View the resulting grouped data
print(grouped_data)Output:
Country
Australia 20.5
Canada 30.5
USA 28.5
Name: Age, dtype: float64In the above example, we created a pandas DataFrame with three columns (‘Name’, ‘Age’, and ‘Country’). We then grouped the data by Country and calculated the average age of each group.
We used the groupby() function to group the data and applied the mean() function to calculate the average age for each group. Finally, we displayed the resulting grouped data.
Conclusion
In this article expansion, we have discussed how to filter rows in pandas DataFrame based on a condition and how to group data in pandas DataFrame. We have provided examples of the syntax used in both cases to make it easier to understand and apply the concepts.
We hope that with this article expansion, you are now better equipped to filter and group data in pandas and are better placed to carry out your data analysis projects.