Adventures in Machine Learning

Mastering Key Operations in Pandas: Selection Filtering Sorting and More

Pandas is a powerful library for data manipulation and analysis in Python. Its ability to handle large datasets and blend various data sources into a single dataframe makes it a popular choice among data scientists and analysts.

In this article, we will explore two key topics: getting top N rows by group in a Pandas dataframe and creating a Pandas dataframe.

Getting Top N Rows by Group in a Pandas Dataframe

Suppose you have a dataset with multiple groups and you would like to retrieve the top N rows from each group. Pandas enables you to perform this task seamlessly.

In this section, we will discuss the syntax for getting top N rows by group, provide examples of getting top N rows grouped by one and multiple columns, and recommend additional resources for Pandas tutorials and common operations.

Syntax for getting top N rows by group

To retrieve the top N rows by group, you can use the `groupby` and `apply` methods in Pandas. The `groupby` method groups the dataframe by the chosen column(s), and the `apply` method applies the function to retrieve the top N rows.

The general syntax for getting top N rows by group is as follows:

“`python

df.groupby(‘column_name’).apply(lambda x: x.nlargest(N, ‘column_sortby’))

“`

In this syntax, `column_name` is the name of the column(s) by which you want to group the dataframe. `column_sortby` is the column by which to sort the groups.

`N` represents the number of rows to retrieve for each group. Example 1: Getting top N rows grouped by one column

Suppose your dataset contains purchase data for multiple products and you wish to see the top 5 purchases for each product type.

Let’s create a sample dataset to demonstrate how to do this. “`python

import pandas as pd

data = {‘Product Type’: [‘phone’, ‘phone’, ‘phone’, ‘laptop’, ‘laptop’, ‘tablet’, ‘tablet’, ‘tablet’],

‘Product Name’: [‘Samsung Galaxy S21’, ‘Apple iPhone 12 Pro Max’, ‘OnePlus 9 Pro’, ‘Dell XPS 15’,

‘Apple MacBook Pro’, ‘Microsoft Surface Pro 7’, ‘Amazon Fire HD 10’, ‘Lenovo Tab M8’],

‘Purchase Amount ($)’: [850, 1250, 899, 949, 1699, 649, 299, 199]}

df = pd.DataFrame(data)

# group by product type and get top 2 purchases for each type

top2_grouped = df.groupby(‘Product Type’).apply(lambda x: x.nlargest(2, ‘Purchase Amount ($)’))

print(top2_grouped)

“`

The code above will output the following:

“`python

Product Type Product Name Purchase Amount ($)

Product Type

laptop 4 laptop Apple MacBook Pro 1699

3 laptop Dell XPS 15 949

phone 1 phone Apple iPhone 12 Pro Max 1250

0 phone Samsung Galaxy S21 850

tablet 5 tablet Microsoft Surface Pro 7 649

6 tablet Amazon Fire HD 10 299

“`

The resulting dataframe shows the top two purchases for each product type. Notice that the lambda function is used with the `nlargest` function to retrieve the two largest values for each group.

Example 2: Getting top N rows grouped by multiple columns

You can use the `groupby` method with multiple columns to group the dataframe by more than one column. Let’s revisit the previous example and group the dataframe by both `Product Type` and `Product Name`, and retrieve the top 1 purchase for each group.

“`python

# group by product type and product name and get top 1 purchase for each group

top1_grouped = df.groupby([‘Product Type’, ‘Product Name’]).apply(lambda x: x.nlargest(1, ‘Purchase Amount ($)’))

print(top1_grouped)

“`

The output will be:

“`python

Product Type Product Name Purchase Amount ($)

Product Type Product Name

laptop Apple MacBook Pro laptop Apple MacBook Pro 1699

Dell XPS 15 laptop Dell XPS 15 949

phone Apple iPhone 12 Pro Max phone Apple iPhone 12 Pro Max 1250

OnePlus 9 Pro phone OnePlus 9 Pro 899

Samsung Galaxy S21 phone Samsung Galaxy S21 850

tablet Amazon Fire HD 10 tablet Amazon Fire HD 10 299

Lenovo Tab M8 tablet Lenovo Tab M8 199

Microsoft Surface Pro 7 None Microsoft Surface Pro 7 649

“`

The resulting dataframe shows the top purchase from the `Product Type` and `Product Name` groups.

Additional Resources

If you’re new to Pandas and want to learn more about how to use it for data analysis and manipulation, there are plenty of tutorials available. We recommend the following resources:

– Pandas Documentation: The official documentation for Pandas is comprehensive and well-organized.

It’s the best place to start if you want to learn more about Pandas. – Pandas Tutorials on DataCamp: DataCamp provides excellent courses on Pandas, including beginner and advanced lessons.

They also provide interactive coding exercises to deepen your understanding. – 10 Minutes to Pandas: This is a quick and easy tutorial for those who want a brief introduction to Pandas.

Creating a Pandas DataFrame

Creating a Pandas dataframe is a fundamental task when working with data in Python. In this section, we will discuss the syntax for creating a dataframe, provide examples of creating a dataframe, and recommend additional resources for Pandas tutorials and common operations.

Syntax for creating a DataFrame

To create a Pandas dataframe, you can use the `pd.DataFrame()` constructor. The general syntax for creating a dataframe is as follows:

“`python

import pandas as pd

df = pd.DataFrame({‘Column1’: [‘Value1’, ‘Value2’, …],

‘Column2’: [‘Value1’, ‘Value2’, …],

… })

“`

In the syntax above, ‘Column1’, ‘Column2’, are the names of the columns, and [‘Value1’, ‘Value2’, …] are the values of each column.

Example of creating a DataFrame

Suppose you have a list of dictionaries representing different cities and their population in millions. Let’s create a dataframe from this data to demonstrate how it works.

“`python

import pandas as pd

cities = [{‘City’: ‘New York’, ‘Population’: 8.336},

{‘City’: ‘Los Angeles’, ‘Population’: 3.979},

{‘City’: ‘Chicago’, ‘Population’: 2.693},

{‘City’: ‘Houston’, ‘Population’: 2.320},

{‘City’: ‘Phoenix’, ‘Population’: 1.680}]

df = pd.DataFrame(cities)

print(df)

“`

The output will be:

“`python

City Population

0 New York 8.336

1 Los Angeles 3.979

2 Chicago 2.693

3 Houston 2.320

4 Phoenix 1.680

“`

The resulting dataframe shows the `City` and `Population` columns from the list of dictionaries.

Additional Resources

If you’re new to Pandas and want to learn more about how to use it for data analysis and manipulation, there are plenty of tutorials available. We recommend the following resources:

– Pandas Documentation: The official documentation for Pandas is comprehensive and well-organized.

It’s the best place to start if you want to learn more about Pandas. – Pandas Tutorials on DataCamp: DataCamp provides excellent courses on Pandas, including beginner and advanced lessons.

They also provide interactive coding exercises to deepen your understanding. – 10 Minutes to Pandas: This is a quick and easy tutorial for those who want a brief introduction to Pandas.

Conclusion

In this article, we explored two key topics in Pandas: getting top N rows by group in a Pandas dataframe and creating a Pandas dataframe. We discussed the syntax for each topic, provided examples of how to use them, and recommended additional resources for Pandas tutorials and common operations.

We hope this article was informative and helps you in your data analysis endeavors. In this article, we will discuss two important operations in Pandas: selecting data from a Pandas dataframe and filtering data in a Pandas dataframe.

These operations are essential for data manipulation and analysis. By learning how to select and filter data, you can extract meaningful insights from your datasets efficiently.

Selecting Data from a Pandas DataFrame

Data selection is the process of retrieving a subset of data from a DataFrame. Pandas allows you to select data based on conditions, columns, and indexes.

In this section, we will discuss the syntax for selecting data from a dataframe, provide examples of selecting data from a dataframe, and recommend additional resources for Pandas tutorials and common operations.

Syntax for selecting data from a DataFrame

To select data from a Pandas dataframe, you can use the following syntax. “`python

df.loc[row_labels, column_labels]

“`

Here, `df` is the name of the Pandas dataframe, `row_labels` and `column_labels` are the row labels and columns labels that you want to select, respectively.

If you want to select all the rows, you can use a colon “:” in place of `row_labels`. Similarly, if you want to select all the columns, you can use a colon “:” in place of `column_labels`.

Example of selecting data from a DataFrame

Let’s create a sample dataframe and illustrate how to select data from it. “`python

import pandas as pd

data = {‘Name’: [‘John’, ‘Mary’, ‘Ben’, ‘Tom’],

‘Age’: [25, 21, 31, 19],

‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’, ‘Chicago’]}

df = pd.DataFrame(data)

print(df.loc[0:2, ‘Name’:’Age’])

“`

The output will be:

“`python

Name Age

0 John 25

1 Mary 21

2 Ben 31

“`

This code selects the rows with index labels from `0` to `2` and the columns from `Name` to `Age`.

Additional Resources

If you’re new to Pandas and want to learn more about how to use it for data analysis and manipulation, there are plenty of tutorials available. We recommend the following resources:

– Pandas Documentation: The official documentation for Pandas is comprehensive and well-organized.

It’s the best place to start if you want to learn more about Pandas. – Pandas Tutorials on DataCamp: DataCamp provides excellent courses on Pandas, including beginner and advanced lessons.

They also provide interactive coding exercises to deepen your understanding. – 10 Minutes to Pandas: This is a quick and easy tutorial for those who want a brief introduction to Pandas.

Filtering Data in a Pandas DataFrame

Data filtering is the process of selecting rows that match a specific criterion from a Pandas dataframe. Pandas filter function enables you to filter rows based on conditions.

In this section, we will discuss the syntax for filtering data in a dataframe, provide examples of filtering data in a dataframe, and recommend additional resources for Pandas tutorials and common operations.

Syntax for filtering data in a DataFrame

To filter data from a Pandas dataframe, you can use the following syntax. “`python

df[df[‘column_name’] < value]

“`

Here, `df` is the name of the Pandas dataframe, `column_name` is the name of the column by which you want to filter the data, and `value` is the value that you want to use as a condition for filtering.

Example of filtering data in a DataFrame

Let’s create a sample dataframe and demonstrate how to filter data from it. “`python

import pandas as pd

data = {‘Name’: [‘John’, ‘Mary’, ‘Ben’, ‘Tom’],

‘Age’: [25, 21, 31, 19],

‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’, ‘Chicago’]}

df = pd.DataFrame(data)

filtered_df = df[df[‘Age’] > 21]

print(filtered_df)

“`

The output will be:

“`python

Name Age City

0 John 25 New York

2 Ben 31 Los Angeles

“`

This code filters records from the `Age` column where the value is greater than 21.

Additional Resources

If you’re new to Pandas and want to learn more about how to use it for data analysis and manipulation, there are plenty of tutorials available. We recommend the following resources:

– Pandas Documentation: The official documentation for Pandas is comprehensive and well-organized.

It’s the best place to start if you want to learn more about Pandas. – Pandas Tutorials on DataCamp: DataCamp provides excellent courses on Pandas, including beginner and advanced lessons.

They also provide interactive coding exercises to deepen your understanding. – 10 Minutes to Pandas: This is a quick and easy tutorial for those who want a brief introduction to Pandas.

Conclusion

In this article, we discussed two important operations in Pandas: selecting data from a Pandas dataframe and filtering data in a Pandas dataframe. The ability to select and filter data is crucial for data manipulation and analysis.

By following the syntax and examples provided, you can extract meaningful insights from your datasets efficiently. We hope this article was informative and helps you in your data analysis endeavors.

Sorting Data in a Pandas DataFrame

Sorting data in a Pandas DataFrame is a common task in data manipulation and analysis. Sorting can help you identify trends, outliers, and patterns in your data.

In this article, we will discuss the syntax for sorting data in a DataFrame, provide examples of sorting data in a DataFrame, and recommend additional resources for Pandas tutorials and common operations. Syntax for

Sorting Data in a Pandas DataFrame

To sort a Pandas dataframe, you can use the `sort_values()` method.

The syntax for sorting data in a Pandas dataframe is as follows:

“`python

df.sort_values(by=[‘column_name’], ascending=[True/False])

“`

Here, `df` is the name of the Pandas dataframe, `column_name` is the name of the column by which you want to sort the data, and `ascending` parameter is used to specify whether to sort the values in ascending or descending order. Example of

Sorting Data in a Pandas DataFrame

Let’s create a sample dataframe and demonstrate how to sort the data in it:

“`python

import pandas as pd

data = {‘Name’: [‘John’, ‘Mary’, ‘Ben’, ‘Tom’],

‘Age’: [25, 21, 31, 19],

‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’, ‘Chicago’]}

df = pd.DataFrame(data)

sorted_df = df.sort_values(by=[‘Age’], ascending=[False])

print(sorted_df)

“`

The output will be:

“`python

Name Age City

2 Ben 31 Los Angeles

0 John 25 New York

1 Mary 21 San Francisco

3 Tom 19 Chicago

“`

This code sorts the rows in the dataframe by the `Age` column in descending order.

Additional Resources

If you’re new to Pandas and want to learn more about how to use it for data analysis and manipulation, there are plenty of tutorials available. We recommend the following resources:

– Pandas Documentation: The official documentation for Pandas is comprehensive and well-organized.

It’s the best place to start if you want to learn more about Pandas. – Pandas Tutorials on DataCamp: DataCamp provides excellent courses on Pandas, including beginner and advanced lessons.

They also provide interactive coding exercises to deepen your understanding. – 10 Minutes to Pandas: This is a quick and easy tutorial for those who want a brief introduction to Pandas.

Conclusion

Sorting data in a Pandas DataFrame is a fundamental operation in data manipulation and analysis. The `sort_values()` method

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