Adventures in Machine Learning

Mastering Data Analysis: Advanced Techniques with Pandas DataFrame

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:

“`python

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

Lets illustrate subtracting two columns and assigning the result to a new column using an example. “`python

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 -9

“`

In 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.

There are two subtopics we will consider under this example:

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:

“`python

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:

“`python

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 -10.0

“`

In 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:

“`python

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

Lets illustrate creating a pandas DataFrame with specified columns and values using an example. “`python

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 UK

“`

In 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.

In this section, we will discuss various ways of viewing pandas DataFrame(s).

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:

“`python

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:

“`python

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 UK

“`

In 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:

“`python

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 32

“`

In 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.

In this section, we will discuss how to import data to pandas DataFrame.

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:

“`python

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, `` is the method from pandas that we will use to import the data, and `` is the path to the 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:

“`python

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.

In this section, we will discuss the syntax for filtering rows in a DataFrame by a particular criterion and provide an example.

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:

“`python

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:

“`python

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 UK

“`

In 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.

In this section, we will discuss the syntax for grouping data in a DataFrame and provide an example of grouping data and applying a function.

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:

“`python

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:

“`python

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: float64

“`

In 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

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