Adventures in Machine Learning

Mastering Partial Matching to Select Columns in Pandas DataFrame

Selecting Columns based on Partial Matching in Pandas DataFramePandas is a powerful library in Python for data manipulation and analysis. It provides functionalities to read, write, and manage data in different formats such as CSV, Excel, SQL databases, and more.

One of the key features of Pandas is its DataFrame structure that lets users organize, manipulate, and query data in a tabular format. One common operation while working with Pandas DataFrame is selecting columns based on their names.

However, sometimes, the column names might not match exactly with what we are looking for, or we might want to select multiple columns that share a common string in their name. In that case, we can use partial matching to select columns in Pandas DataFrame.

In this article, we will explore two methods for selecting columns based on partial matching and provide examples to illustrate their usage. Method 1: Selecting Columns Based on One Partial Match

The first method we will look at is selecting columns based on one partial match.

This means selecting all columns that contain a specific string in their names. To do this, we can use the Pandas DataFrame filter function with a regex pattern that matches the desired string.

The filter function takes a `like` argument that accepts a string or a regular expression pattern that defines the columns to be selected. For instance, if we have a DataFrame with column names [‘apple’, ‘banana’, ‘orange’, ‘kiwi’] and we want to select all columns that contain the string ‘an’, we can use the following code:

“`python

import pandas as pd

df = pd.DataFrame({‘apple’: [1, 2, 3], ‘banana’: [4, 5, 6], ‘orange’: [7, 8, 9], ‘kiwi’: [10, 11, 12]})

df_filtered = df.filter(like=’an’)

print(df_filtered)

“`

The output will be a DataFrame with columns ‘banana’ and ‘orange’. As we can see, the filter function returns all columns that contain the specified string.

This method can be useful when we want to select a subset of columns that share a common string in their names. Method 2: Selecting Columns Based on Multiple Partial Matches

The second method we will look at is selecting columns based on multiple partial matches.

This means selecting all columns that contain any of the specified strings in their names. To do this, we can use a list comprehension to iterate over the DataFrame columns and check if each column name contains any of the specified strings.

We can then concatenate the resulting columns into a new DataFrame using the Pandas `concat` function. For instance, if we have a DataFrame with column names [‘apple’, ‘banana’, ‘orange’, ‘kiwi’] and we want to select all columns that contain either ‘a’ or ‘i’, we can use the following code:

“`python

import pandas as pd

df = pd.DataFrame({‘apple’: [1, 2, 3], ‘banana’: [4, 5, 6], ‘orange’: [7, 8, 9], ‘kiwi’: [10, 11, 12]})

columns_to_select = [‘a’, ‘i’]

df_filtered = pd.concat([df[col] for col in df.columns if any(x in col for x in columns_to_select)], axis=1)

print(df_filtered)

“`

The output will be a DataFrame with columns ‘apple’, ‘orange’, and ‘kiwi’. As we can see, the list comprehension iterates over all columns in the DataFrame and checks if each column name contains any of the specified strings.

It then selects only the columns that meet the condition and concatenates them into a new DataFrame. Example 1: Selecting Columns Based on One Partial Match

Let’s consider an example where we have a DataFrame with column names representing the names of different countries and their populations in millions.

“`python

import pandas as pd

data = {‘USA (millions)’: [328.2, 330.9, 333.7, 336.1],

‘India (millions)’: [1237.3, 1252.1, 1267.4, 1281.9],

‘China (millions)’: [1393.8, 1398.3, 1398.5, 1399.3],

‘Brazil (millions)’: [207.7, 209.5, 211.4, 213.3]}

df = pd.DataFrame(data)

“`

Suppose we want to select all columns that contain the string ‘ind’. We can use the filter function to achieve this:

“`python

df_filtered = df.filter(like=’ind’)

print(df_filtered)

“`

The output will be a DataFrame with column ‘India (millions)’:

“`

India (millions)

0 1237.3

1 1252.1

2 1267.4

3 1281.9

“`

As we can see, the filter function selects only the column that contains the specified string in its name. Conclusion:

In this article, we have learned two methods for selecting columns based on partial matching in Pandas DataFrame.

The first method filters columns that contain a specific string in their names using the filter function. The second method selects columns that contain any of the specified strings in their names using a list comprehension and the concat function.

These methods can be useful when we want to manipulate data based on specific patterns in their column names. Overall, Pandas provides a powerful toolset for data manipulation that can help us perform complex data analysis with ease.

Example 2: Selecting Columns Based on Multiple Partial Matches

Now, let’s look at an example of selecting columns based on multiple partial matches. Suppose we have a DataFrame that contains information about different movies, including their titles, release years, genres, and ratings.

We want to select all columns that contain the strings ‘title’, ‘year’, or ‘rating’ in their names. We can use the following code:

“`python

import pandas as pd

data = {‘movie_title’: [‘The Shawshank Redemption’, ‘The Godfather’, ‘The Dark Knight’, ’12 Angry Men’],

‘release_year’: [1994, 1972, 2008, 1957],

‘genre’: [‘Drama’, ‘Crime, Drama’, ‘Action, Crime, Drama’, ‘Drama’],

‘rating_imdb’: [9.3, 9.2, 9.0, 8.9],

‘rating_rotten’: [90, 98, 94, 100]}

df = pd.DataFrame(data)

columns_to_select = [‘title’, ‘year’, ‘rating’]

df_filtered = pd.concat([df[col] for col in df.columns if any(x in col.lower() for x in columns_to_select)], axis=1)

print(df_filtered)

“`

The output will be a DataFrame with columns ‘movie_title’, ‘release_year’, ‘rating_imdb’, and ‘rating_rotten’:

“`

movie_title release_year rating_imdb rating_rotten

0 The Shawshank Redemption 1994 9.3 90

1 The Godfather 1972 9.2 98

2 The Dark Knight 2008 9.0 94

3 12 Angry Men 1957 8.9 100

“`

As we can see, the list comprehension iterates over all columns in the DataFrame and checks if each column name contains any of the specified strings in lowercase. It then selects only the columns that meet the condition and concatenates them into a new DataFrame.

In this example, we used the `lower()` method to convert the column names to lowercase to avoid case-sensitive matching.

Additional Resources

To learn more about Pandas and its common operations, several resources are available online. Here are some tutorials and documentation worth checking out:

1.

Official Pandas Documentation – The official documentation provides comprehensive information about the different functions, classes, and modules in Pandas. It covers a wide range of topics, including data manipulation, merging, filtering, and more.

2. Kaggle Tutorials – Kaggle is a popular platform for data science competitions and tutorials.

They have a range of tutorials on Pandas, including data cleaning and manipulation, visualization, and machine learning. 3.

DataCamp Tutorials – DataCamp is an online learning platform that provides interactive courses and tutorials on data science topics, including Pandas. They offer tutorials on foundational topics and advanced topics such as time series analysis and big data with Pandas.

4. Real Python Tutorials – Real Python is a community-driven platform that provides in-depth tutorials on Python programming.

They have a range of tutorials on Pandas, including data manipulation, plotting, and statistical analysis. By exploring these resources, you’ll be able to enhance your knowledge of Pandas and its common operations, enabling you to become more proficient at managing and analyzing data.

In conclusion, selecting columns in Pandas DataFrame based on partial matching is a powerful data manipulation technique that allows users to retrieve specific data based on patterns in the column names. With the filter function and list comprehension, we can efficiently select columns that contain a specific string or multiple strings in their names.

The importance of this technique cannot be overemphasized in data science and analysis, where organizing, manipulating, and querying data is critical. As such, Python developers should take advantage of the available resources such as tutorials, documentation, and interactive courses to enhance their knowledge on the subject matter.

With these resources, Python developers can master Pandas’ advanced functionalities, providing them with a competitive advantage in the field of data analytics.

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