✅ Top Python Libraries for Data Analytics 📊🐍

1. Pandas – Data Handling & Analysis
- Work with tabular data using DataFrames
- Clean, filter, group, and aggregate data
- Read/write from CSV, Excel, JSON

import pandas as pd
df = pd.read_csv("sales.csv")
print(df.head())

2. NumPy – Numerical Operations
- Efficient array and matrix operations
- Used for data transformation and statistical tasks

import numpy as np
arr = np.array([10, 20, 30])
print(arr.mean())  # 20.0

3. Matplotlib – Basic Visualization
- Create line, bar, scatter, and pie charts
- Customize titles, legends, and styles

import matplotlib.pyplot as plt
plt.bar(["A", "B", "C"], [10, 20, 15])
plt.show()

4. Seaborn – Statistical Visualization
- Heatmaps, box plots, histograms, and more
- Easy integration with Pandasimport seaborn as sns
sns.boxplot(data=df, x="Region", y="Revenue")


5. Plotly – Interactive Graphs
- Zoom, hover, and export visuals
- Great for dashboards and presentationsimport plotly.express as px
fig = px.line(df, x="Month", y="Sales")
fig.show()


6. Scikit-learn – Machine Learning for Analysis
- Feature selection, classification, regression
- Data preprocessing & model evaluation

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

7. Statsmodels – Statistical Analysis
- Perform regression, ANOVA, time series analysis
- Great for data exploration and insight extraction

8. OpenPyXL / xlrd – Excel File Handling
- Read/write Excel files with formulas, formatting, etc.

💡 Pro Tip: Combine Pandas, Seaborn, and Scikit-learn to build complete analytics pipelines.

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