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Publisher : Packt Publishing; 2nd ed. edition (May 31, 2024)
Language : English
Paperback : 486 pages
ISBN-10 : 1803239875
ISBN-13 : 978-1803239873
Item Weight : 1.85 pounds
Dimensions : 1.15 x 7.5 x 9.25 inches
Data cleaning is an essential step in any data analysis project. In order to effectively analyze and derive insights from your data, it is crucial to first clean and preprocess it. In this post, we will walk through a Python Data Cleaning Cookbook, using popular libraries such as pandas, NumPy, Matplotlib, scikit-learn, and OpenAI.
1. Importing the necessary libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import openai
2. Loading the dataset:
df = pd.read_csv(‘data.csv’)
3. Handling missing values:
# Check for missing values
print(df.isnull().sum())
# Fill missing values with mean
df.fillna(df.mean(), inplace=True)
4. Removing duplicates:
# Remove duplicate rows
df.drop_duplicates(inplace=True)
5. Handling outliers:
# Detect and remove outliers using Z-score
z_scores = np.abs(stats.zscore(df))
df = df[(z_scores < 3).all(axis=1)]
6. Standardizing the data:
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)
7. Visualizing the data:
# Plot a histogram of a numerical column
plt.hist(df[‘column_name’])
plt.xlabel(‘Column Name’)
plt.ylabel(‘Frequency’)
plt.title(‘Histogram of Column Name’)
plt.show()
8. Text data cleaning:
# Clean text data using OpenAI’s GPT-3
clean_text = openai.api.text_completion(
model=”text-davinci-003″,
prompt=”Clean the text data: ” + text_data
)
By following these steps and using the powerful capabilities of libraries such as pandas, NumPy, Matplotlib, scikit-learn, and OpenAI, you can effectively prepare your data for analysis and derive meaningful insights. Happy data cleaning!
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