Run this notebook: Open in Colab Open in Kaggle

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


marks_data = [go.Bar(x=production.index, y=production["2000-01"])]

plotly.offline.plot({ 'data': marks_data,
            'layout': {
               'title': 'Marks Distribution',
               'xaxis': {
                 'title': 'Items'},
               'yaxis': {
                'title': 'Year '}
        }})

India Production Data – Advanced Plotly Visualizations

This project creates a comprehensive set of interactive Plotly visualizations for India’s electronic production data. It covers bar charts for single-category analysis, multi-trace scatter plots comparing 10+ production categories simultaneously (mobile handsets, software, hardware, consumer electronics), interactive data tables with plotly.figure_factory, 2D histogram contour plots, Cufflinks integration for one-line plotting, and Seaborn statistical visualizations (distribution and box plots).

Why this matters: Real-world data presentations often require multiple visualization types tailored to different audiences and questions. This project demonstrates the full spectrum of Plotly capabilities – from simple bar charts to advanced contour plots – showing how to choose the right chart type for each analytical question. The multi-trace scatter plot comparing all production sectors over time is particularly valuable for identifying which industries are growing fastest.

import pandas as pd

production = pd.read_csv("mobile.csv", index_col="Item")

production.head()

production.index

production["2000-01"]

production.head()

production[["2000-01","2014-15"]]

production.groupby("")

import plotly.plotly as py
import plotly.graph_objs as go

# Create random data with numpy
import numpy as np

N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N)+5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N)-5

# Create traces
trace0 = go.Scatter(
    x = random_x,
    y = random_y0,
    mode = 'markers',
    name = 'markers'
)
trace1 = go.Scatter(
    x = random_x,
    y = random_y1,
    mode = 'lines+markers',
    name = 'lines+markers'
)
trace2 = go.Scatter(
    x = random_x,
    y = random_y2,
    mode = 'lines',
    name = 'lines'
)

data = [trace0, trace1, trace2]
plotly.offline.plot(data, filename='scatter-mode.html')

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as FF

import numpy as np
import pandas as pd
df = pd.read_csv('mobile.csv')

sample_data_table = FF.create_table(df.head())
plotly.offline.plot(sample_data_table, filename='sample-data-table.html')

import plotly.tools as tls
tls.embed('https://plot.ly/~cufflinks/8')

from plotly.graph_objs import *
import numpy as np

x = np.random.randn(2000)
y = np.random.randn(2000)
plotly.offline.plot([Histogram2dContour(x=x, y=y, contours=Contours(coloring='heatmap')),
       Scatter(x=x, y=y, mode='markers', marker=Marker(color='white', size=3, opacity=0.3))], show_link=False)

import cufflinks as cf
plotly.offline.plot(cf.datagen.lines().iplot(asFigure=True,
                               kind='scatter',xTitle='Dates',yTitle='Returns',title='Returns'))

import plotly.plotly as py 

fig = py.get_figure('https://plot.ly/~jackp/8715', raw=True)
plotly.offline.plot(fig)

mobile = pd.read_csv("mobile.csv", index_col="Item")

mobile.head()

mobile_t = mobile.transpose()

mobile_t.head()

mobile_t["Mobile Handsets"].nunique()

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


mobile_data = [go.Bar(x=mobile_t.index, y=mobile_t["Mobile Handsets"])]

plotly.offline.plot({ 'data': mobile_data,
            'layout': {
               'title': 'Mobile Production India',
               'xaxis': {
                 'title': 'Year'},
               'yaxis': {
                'title': 'Sales '}
        }})

output = None

production = pd.read_csv("production.csv", index_col="Item")

production_t = production.transpose()

production_t.head()

df = df.drop('2001-02', 0)

production_t = df

df

import plotly.plotly as py
import plotly.graph_objs as go

# Create random data with numpy
import numpy as np

N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N)+5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N)-5

# Create traces
trace0 = go.Scatter(
    x = random_x,
    y = random_y0,
    mode = 'lines',
    name = 'lines'
)
trace1 = go.Scatter(
    x = random_x,
    y = random_y1,
    mode = 'lines+markers',
    name = 'lines+markers'
)
trace2 = go.Scatter(
    x = random_x,
    y = random_y2,
    mode = 'markers',
    name = 'markers'
)
data = [trace0, trace1, trace2]

plotly.offline.plot(data, filename='line-mode')

import plotly.plotly as py
import plotly.graph_objs as go

# Create random data with numpy
import numpy as np

# Create traces
trace0 = go.Scatter(
    x=production_t.index, y=production_t["Mobile Handsets"],
    mode = 'lines',
    name = 'Mobile Handsets'
)
trace1 = go.Scatter(
    x=production_t.index, y=production_t["Sub-Total (Software)"],
    mode = 'lines+markers',
    name = 'Sub-Total (Software)'
)
trace2 = go.Scatter(
    x=production_t.index, y=production_t["Software for Exports"],
    mode = 'markers',
    name = 'Software for Exports'
)

trace3 = go.Scatter(
    x=production_t.index, y=production_t["Consumer Electronics"],
    mode = 'lines',
    name = 'Consumer Electronics'
)
trace4 = go.Scatter(
    x=production_t.index, y=production_t["Industrial Electronics"],
    mode = 'lines+markers',
    name = 'Industrial Electronics'
)
trace5 = go.Scatter(
    x=production_t.index, y=production_t["Computer Hardware"],
    mode = 'markers',
    name = 'Computer Hardware'
)
trace6 = go.Scatter(
    x=production_t.index, y=production_t["Strategic Electronics"],
    mode = 'lines+markers',
    name = 'Strategic Electronics'
)
trace7 = go.Scatter(
    x=production_t.index, y=production_t["Electronics Components"],
    mode = 'markers',
    name = 'Electronics Components'
)

trace9 = go.Scatter(
    x=production_t.index, y=production_t["Sub-Total (Hardware)"],
    mode = 'lines+markers',
    name = 'Sub-Total (Hardware)'
)
trace10 = go.Scatter(
    x=production_t.index, y=production_t["Domestic Software"],
    mode = 'markers',
    name = 'Domestic Software'
)



data = [trace0, trace1, trace2,trace3, trace4, trace5,trace6, trace7,trace9,trace10]

plotly.offline.plot(data, filename='line-mode')

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


mobile_data = [go.Bar(x=df.index, y=df["Mobile Handsets"])]

print(mobile_data)

plotly.offline.plot({ 'data': mobile_data,
            'layout': {
               'title': 'Mobile Production India',
               'xaxis': {
                 'title': 'Year'},
               'yaxis': {
                'title': 'Sales '}
        }})

df["Mobile Handsets"]

import seaborn as sns
sns.set(color_codes=True)

%matplotlib inline

sns.distplot(mobile_t)

mobile_t

sns.set_style("whitegrid")
sns.boxplot(data=mobile_t);