Table of Contents
Data cleaning in Puthon is an essential step in the data science process. It ensures the accuracy and quality of data, which greatly impacts data analysis results and learn How to do Data Cleaning in python
1. Handling Missing Values and How to do Data Cleaning in python
Missing data is a common issue in datasets. We can handle missing data in several ways:
- Deleting Rows: This method is advised only when the rows with missing values are not significant.
Python
import pandas as pd
# Load your dataset
df = pd.read_csv(‘your_dataset.csv’)
# Remove rows with missing values
df = df.dropna()
- Imputation: Replacing missing values with statistical measures like mean, median, or mode.
Python
# Replace missing values with mean
df = df.fillna(df.mean())
2. Removing Duplicates
Duplicate data can skew your analysis. It’s essential to identify and remove duplicates:
Python
# Remove duplicates
df = df.drop_duplicates()

3. Data Type Conversion
Sometimes, the data types of columns might not be appropriate. We can convert data types as needed:
Python
# Convert the data type of a column to a numeric
df[‘column_name’] = pd.to_numeric(df[‘column_name’])
4. Renaming Columns
For better understanding, we might need to rename the columns:
Python
# Rename columns
df = df.rename(columns={‘old_name’: ‘new_name’})
5. Outlier Detection
Outliers can significantly affect your results. They can be detected using methods like the IQR score:
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 – Q1
# Remove outliers
df = df[~((df < (Q1 – 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]
Remember, data cleaning is highly specific to the dataset you’re working with. Always understand your data thoroughly before deciding on the appropriate cleaning methods.
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