Python data wrangling examples

Yvette 160 Published: 06/23/2024

Python data wrangling examples

I'll respond in English only this time, since you asked for it!

Now, let's dive into some Python data wrangling examples.

Example 1: Handling Missing Values

Suppose we have a dataset with missing values:

import pandas as pd

data = {'Name': ['John', 'Jane', np.nan, 'Bob'],


'Age': [25, 30, 35, 40],


'Score': [80, 70, np.nan, 90]}


df = pd.DataFrame(data)


print(df)

In this example, we have missing values (np.nan) for John's Age and Jane's Score. We can use the fillna() method to replace these missing values with a specific value (e.g., mean or median):

# Replace missing values with the mean of each column

df.fillna(df.mean(), inplace=True)


print(df)

Example 2: Data Cleaning

Let's say we have a dataset with inconsistent data formats:

data = {'ID': ['123', 'abc', '456', 'def'],

'Date': ['2020-01-01', 'January 1, 2020', '2020-02-15', np.nan]}


df = pd.DataFrame(data)


print(df)

In this case, we can use the pd.to_datetime() function to convert the Date column to a consistent format:

# Convert Date column to datetime format

df['Date'] = pd.to_datetime(df['Date'])


print(df)

Example 3: Data Transformation

Suppose we have a dataset with categorical variables that need to be transformed into numerical variables:

data = {'Category': ['A', 'B', 'C', 'D'],

'Value': [10, 20, 30, 40]}


df = pd.DataFrame(data)


print(df)

In this example, we can use the pd.get_dummies() function to transform the Category column into numerical variables:

# Transform categorical variable into numerical variables

dummies = pd.get_dummies(df['Category'])


print(dummies)

These are just a few examples of data wrangling in Python. The pandas library provides many more features for handling missing values, cleaning data, and transforming data.

Do you have any specific questions or areas you'd like me to cover?

Python data wrangling cheat sheet

Here's a comprehensive Python data wrangling cheat sheet:

Importing Libraries

pandas (pd): For efficient data manipulation and analysis. numpy (np): For numerical operations and data structures.

Loading Data

pd.read_csv('file.csv'): Load a CSV file into a pandas DataFrame. pd.read_excel('file.xlsx'): Load an Excel file into a pandas DataFrame. pd.read_json('file.json'): Load a JSON file into a pandas DataFrame.

Data Inspection

df.head(): Display the first few rows of a DataFrame. df.info(): Display summary information about a DataFrame. df.describe(): Generate descriptive statistics for each column.

Data Cleaning

pd.isna(df): Identify missing values in a DataFrame. df.fillna(value) : Fill missing values with a specified value. df.dropna() : Drop rows with missing values. df.replace(to_replace, value) : Replace specific values in a column.

Data Transformation

pd.melt(df): Convert wide data to long format. pd.pivot_table(df, columns='column') : Pivot a DataFrame from wide format to long format. df.groupby('column').sum() : Groupby a column and apply a sum operation.

Data Manipulation

df.append(other_df) : Append one or more DataFrames to another. df.merge(other_df, on='column') : Merge two DataFrames based on a common column. df.sort_values(by='column') : Sort a DataFrame by one or more columns.

Data Visualization

import matplotlib.pyplot as plt; df.plot() : Create a line plot of a DataFrame. import seaborn as sns; sns.pairplot(df) : Create a scatter plot matrix for a DataFrame.

Common Tasks

df.set_index('column') : Set a column as the index for a DataFrame. df.reset_index(drop=True) : Reset the index of a DataFrame. pd.to_datetime(df['date']) : Convert a datetime-like column to datetime format.

Error Handling

try-except statements: Handle errors and exceptions in your code.

Remember to explore the documentation for each library and function to learn more about their capabilities and limitations. Happy wrangling!

Related Articles