Post #2326

✅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. Tap ❤️ for more!