Python data cleaning w3schools example

Python data cleaning w3schools example

I'd be happy to provide an example of Python data cleaning using the W3Schools dataset!

The following code snippet is a step-by-step guide on how to clean and preprocess a CSV file (in this case, the W3Schools dataset) using Python:

import pandas as pd
Read in the CSV file
df = pd.read_csv('w3schools.csv')
print("Before cleaning:")
print(df.head())
Remove duplicates: In some cases, you may have duplicate rows in your dataset. You can use the drop_duplicates function to remove them.
df.drop_duplicates(inplace=True)
print("nAfter removing duplicates:")
print(df.head())
Drop any columns that contain missing values
df.dropna(axis=1, how='any', inplace=True)
print("nAfter dropping missing values:")
print(df.head())
Fill in missing values (e.g., imputing mean or median)
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')  # you can change 'mean' to 'median'
df[['Age', 'Fare']] = imputer.fit_transform(df[['Age', 'Fare']])
print("nAfter filling in missing values:")
print(df.head())
Remove rows with missing values (e.g., dropping nulls)
df.dropna(inplace=True)
print("nAfter removing rows with missing values:")
print(df.head())
Handle categorical data:
Replace text strings to numerical or binary format
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
for column in df.columns:
if df[column].dtype == 'object':
df[column] = label_encoder.fit_transform(df[column])
print("nAfter encoding categorical data:")
print(df.head())

In this example:

Remember that this is just an example of basic data cleaning steps and you may need to add more depending on your specific dataset's characteristics and needs!

What are the Python data wrangling libraries?

I'd be happy to help you with that!

Python has a vast array of libraries and tools for data wrangling, which is a crucial step in the data science process. Data wrangling involves cleaning, transforming, and preparing data for analysis. Here are some popular Python data wrangling libraries:

These libraries can significantly streamline and simplify the process of preparing your data for analysis, making it easier to extract insights and gain valuable information from your dataset!