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import pandas as pd
from pandas_datareader import data

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

Electronic Production in India – Data Analysis and Visualization

This project analyzes India’s electronic production data (mobile handsets, consumer electronics, etc.) across multiple years. It demonstrates loading CSV data with a custom index, transposing wide-format DataFrames for time-series analysis, exporting to Excel with pd.ExcelWriter, and creating interactive bar charts with Plotly alongside static Matplotlib line plots.

Why this matters: Government and industry production data is often published in wide format (one column per year), which requires transposition before time-series visualization. This project teaches a common real-world data reshaping pattern and shows how to combine Pandas with both static (Matplotlib) and interactive (Plotly) visualization libraries for different audiences.

production = pd.read_csv("mobile_2.csv")
production_idx = pd.read_csv("mobile_2.csv", index_col="Item", parse_dates=True)

production_idx

production_t = production_idx.transpose()
production_t

path = r"mobile_t.xlsx"

writer = pd.ExcelWriter(path, engine = 'xlsxwriter')
production_t.to_excel(writer)
writer.save()
writer.close()

type(production_t)

import plotly
from plotly.graph_objs import Scatter, Layout
import plotly.graph_objs as go


literate = [go.Bar(x=production_t.index, y=production_t["Mobile Handsets"])]


plotly.offline.plot({ 'data': literate,
            'layout': {
               'title': 'POPULATION LITERACY RATE IN TN',
               'xaxis': {
                 'title': 'YEAR'},
               'yaxis': {
                'title': 'POPULATION (in lakhs) '}
        }})

plt.style.use("ggplot")

production.plot(x="Item")

production_idx.plot()