Day 11 of 30 days of Data Analytics with Projects Series
Welcome back peeps. This is Day 11 of 30 days of data analytics.
What’s covered in the Data Analytics Series till now —
Day 1 : Data Analytics basics and kickstart of Data analytics with projects series
Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)
Day 5 : Statistics
Day 6 : Basic and Advanced SQL
Day 8 : Pandas and Numpy
Day 9 : Data Manipulation
Day 10 : Data Visualization — Part 1
Day 11 : Data Visualization — Part 2
In the last post we covered —
Data Visualization — Part 1
In this post we will cover data visualization — part 2 as follows —
Data Visualization — Part 2
Data Visualization using Matplotlib and Seaborn with project
Data Visualization — Part 3
Data Visualization using Plotly
Data Visualization using Bokeh
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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Data Visualization using Matplotlib and Seaborn
Seaborn is a data visualization which is based on matplotlib. It provides high level interfaces to visualize data.
To import matplotlib and seaborn —
import matplotlib.pyplot as ply
import seaborn as snsTo create plots
Load the dataset
Define some plots aesthetics
Plot with seaborn
Customize — give title, name, axis etc
To load car dataset using seaborn —
df = sns.load_dataset("car")Code Example for different charts —
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Create sample data
data = {
'Category': ['A', 'B', 'C', 'D', 'E'],
'Value': [10, 15, 7, 12, 8]
}
df = pd.DataFrame(data)
# Line Chart
plt.figure(figsize=(8, 6))
plt.plot(df['Category'], df['Value'])
plt.title('Line Chart')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
# Column Chart
plt.figure(figsize=(8, 6))
plt.bar(df['Category'], df['Value'])
plt.title('Column Chart')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
# Histogram
plt.figure(figsize=(8, 6))
plt.hist(df['Value'], bins=10)
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()
# Bar Chart
plt.figure(figsize=(8, 6))
plt.barh(df['Category'], df['Value'])
plt.title('Bar Chart')
plt.xlabel('Value')
plt.ylabel('Category')
plt.show()
# Stacked Column Chart
data = {
'Category': ['A', 'B', 'C', 'D', 'E'],
'Value1': [10, 15, 7, 12, 8],
'Value2': [5, 8, 10, 6, 12]
}
df_stacked = pd.DataFrame(data)
plt.figure(figsize=(8, 6))
plt.bar(df_stacked['Category'], df_stacked['Value1'])
plt.bar(df_stacked['Category'], df_stacked['Value2'], bottom=df_stacked['Value1'])
plt.title('Stacked Column Chart')
plt.xlabel('Category')
plt.ylabel('Value')
plt.legend(['Value1', 'Value2'])
plt.show()
# Pie Chart
plt.figure(figsize=(8, 6))
plt.pie(df['Value'], labels=df['Category'], autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()
# Donut Chart
plt.figure(figsize=(8, 6))
plt.pie(df['Value'], labels=df['Category'], autopct='%1.1f%%', wedgeprops={'edgecolor': 'white'})
plt.title('Donut Chart')
plt.show()
# Area Chart
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(8, 6))
plt.fill_between(x, y, alpha=0.3)
plt.title('Area Chart')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
# Scatter Plot
x = np.random.randn(100)
y = np.random.randn(100)
plt.figure(figsize=(8, 6))
plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
# Box Plot
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.figure(figsize=(8, 6))
plt.boxplot(data)
plt.title('Box Plot')
plt.xlabel('Data')
plt.ylabel('Values')
plt.show()
# KDE Chart
data = np.random.randn(1000)
plt.figure(figsize=(8, 6))
sns.histplot(data, kde=True)
plt.title('KDE Chart')
plt.xlabel('Data')
plt.ylabel('Values')
plt.show()In this we will cover —
Categorical Plots
Regressin Plots
Distribution Plots
Heatmaps
To plot categorical plot —
- Bar plot
sns.barplot(x,y,data,palette)- Scatterplot
sns.swarmplot(x,y,data)- Countplot
sns.countplot(x,data,palette)- Pointplot
sns.pointplot(s,y,data,palette,hue)- Boxplot
sns.boxplot(x,y,data)- Violinplot
sns.violinplot(x,y,data)For Regression plot —
It is used to showa linear regression model fit over the plotted data.
sns.regplot(x,y,data,axis)For Distribution Plots —
It is used to plot univariate distribution
sns.distplot(data,y,kde)For heatmap—
sns.heatmap(uniform_data,vmin,vmax)Project
We will do a project ( using Netflix data) —
Let’s get started!
Import Necessary Libraries
# Import necessary libraries
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
from matplotlib.colors import rgb2hex
import matplotlib.cm as cm
from wordcloud import WordCloud
from PIL import Image
from sklearn.preprocessing import MultiLabelBinarizer
import matplotlib.colorsfrom collections import Counter
cmap2 = cm.get_cmap('twilight',13)
colors1= []
for i in range(cmap2.N):
rgb= cmap2(i)[:4]
colors1.append(rgb2hex(rgb))
# Set style
sns.set(style='whitegrid')Load the Data
# Read data from the CSV using pandas read_csv
df= pd.read_csv('/Path to the File/netflix_titles.csv', low_memory = False)#show data
df.head()Output —
# Get more information about your data
df.info()Output —
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 show_id 8807 non-null object
1 type 8807 non-null object
2 title 8807 non-null object
3 director 6173 non-null object
4 cast 7982 non-null object
5 country 7976 non-null object
6 date_added 8797 non-null object
7 release_year 8807 non-null int64
8 rating 8803 non-null object
9 duration 8804 non-null object
10 listed_in 8807 non-null object
11 description 8807 non-null object
dtypes: int64(1), object(11)
memory usage: 825.8+ KB#Missing Values in each column
df.isna().sum()Output —
show_id 0
type 0
title 0
director 2634
cast 825
country 831
date_added 10
release_year 0
rating 4
duration 3
listed_in 0
description 0
dtype: int64#Count of data records in each columndf.count()Output —
show_id 8807
type 8807
title 8807
director 6173
cast 7982
country 7976
date_added 8797
release_year 8807
rating 8803
duration 8804
listed_in 8807
description 8807
dtype: int64# Unique Values for the type of shows on netflixdf['type'].unique()Output —
array(['Movie', 'TV Show'], dtype=object)# Unique values for the rating
df.rating.unique()Output —
array(['PG-13', 'TV-MA', 'PG', 'TV-14', 'TV-PG', 'TV-Y', 'TV-Y7', 'R','TV-G', 'G', 'NC-17', '74 min', '84 min', '66 min', 'NR', nan,
'TV-Y7-FV', 'UR'], dtype=object)# Comparison between movies and Tv Showsn_shows = df[df['type']=='TV Show']
n_movies = df[df['type']=='Movie']# plot
plt.figure(figsize=(6,4),dpi=100)ax=sns.countplot(x='type',data=df,palette='mako',linewidth=1,edgecolor='black')
plt.xlabel("Content Type on Netflix")
plt.ylabel('Count')
plt.title('Comparison between movies and TV shows')
plt.tight_layout()
plt.show()Output —
#Percent Distribution
ng_type = df['type'].value_counts().reset_index()
ng_type = ng_type.rename(columns = {'type': 'count','index':'type'})t = go.Pie(values=ng_type['count'],labels=ng_type['type'],marker=dict(colors=['LightSkyBlue','MediumPurple']))
layout = go.Layout(height=500,legend=dict(x=0.1,y=1.1))fig = go.Figure(data=[t],layout=layout)
iplot(fig)Output —
# Distribution of Ratingsplt.figure(figsize=(18,12))
p_ratings = df['rating'].value_counts().head(10)
plt.pie(x=p_ratings,labels=p_ratings.index,colors=colors1,autopct='%.0f%%',explode=[0.07 for i in p_ratings.index],startangle=90,wedgeprops={'linewidth':1,'edgecolor':'black'},shadow=True)
plt.title('Ratings Distributions ')
plt.legend(loc='upper right',title='Rating Types')plt.show()Output —
# Ratings Analysisplt.figure(figsize=(15,8))
sns.countplot(x='rating',data=df,palette='mako',hue ='type',order=df['rating'].value_counts().index[0:10])
plt.xlabel('Ratings')
plt.ylabel('Count')
plt.legend()
plt.title('Netflix Content Rating Analysis')plt.show()Output —
# Movies Ratings Analysisplt.figure(figsize=(15,8))
sns.countplot(x='rating',data=n_movies,palette='mako',order=n_movies['rating'].value_counts().index[0:10],edgecolor='black')
plt.xlabel('Movie Ratings')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.title("Movie Ratings Analysis")plt.show()Output —
# Show Ratings Analysisplt.figure(figsize=(15,8))
sns.countplot(x='rating',data=n_shows,palette='mako',order=n_shows['rating'].value_counts().index[0:10],edgecolor='black')
plt.xlabel('Shows Ratings')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.title("Shows Ratings Analysis")plt.show()Output —
# Movies vs TV showsfig,(ax0,ax1)=plt.subplots(1,2,figsize=(30,18))
np_movies = n_movies['rating'].value_counts().head(10)
np_shows = n_shows['rating'].value_counts().head(10)ax0.pie(x=np_movies,labels=np_movies.index,colors=colors1,autopct='%.0f%%',explode=[0.05 for i in np_movies.index],startangle=160,wedgeprops={'linewidth':1,'edgecolor':'black'},shadow=True)
plt.title('Ratings Distribution for Movies (Left) and TV shows (Right)',bbox={'facecolor':'0.9','pad':5},loc='left',fontsize=17)ax1.pie(x=np_shows,labels=np_shows.index,colors=colors1,autopct='%.0f%%',explode=[0.05 for i in np_shows.index],startangle=160,wedgeprops={'linewidth':1,'edgecolor':'black'},shadow=True)plt.show()Output —
# Month when content can be released
n_date=df[['date_added']].dropna()
n_date['year']= n_date['date_added'].apply(lambda x: x.split(', ')[-1])
n_date['month'] = n_date['date_added'].apply(lambda x:x.split(' ')[0])month_list = ['January','February','March','April','May','June','July','August','September','October','November','December']g_df= n_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_list].T# plotplt.figure(figsize=(8,5),dpi=250)
plt.pcolor(g_df,cmap='Purples',edgecolors='white',linewidths=3)
plt.xticks(np.arange(0.8,len(g_df.columns),1),g_df.columns,fontsize=5)
plt.yticks(np.arange(0.8,len(g_df.index),1),g_df.index,fontsize=5)
cbar=plt.colorbar()cbar.ax.tick_params(labelsize=7)
cbar.ax.minorticks_on()plt.show()Output —
# Top 10 countriesdf['country'] = df.country.dropna()
n_countries = df.country.value_counts().head(10)# plot
plt.figure(figsize=(18,10))
plt.pie(x=n_countries,labels=n_countries.index,colors=colors1,autopct='%.0f%%',explode=[0.05 for i in n_countries.index],startangle=160,wedgeprops={'linewidth':1,'edgecolor':'black'},shadow=True)plt.show()Output —
# Which Country produces the most contentn_country = df['country'].dropna()
nc_country = pd.Series(dict(Counter(','.join(n_country).replace(' ,',',').replace(', ',',').split(',')))).sort_values(ascending=False)#get top 15 countries
nc_country[:15]Output —
United States 3690
India 1046
United Kingdom 806
Canada 445
France 393
Japan 318
Spain 232
South Korea 231
Germany 226
Mexico 169
China 162
Australia 160
Egypt 117
Turkey 113
Hong Kong 105
dtype: int64# Plot the top 15 countriesfig = plt.figure(figsize=(16,16))t = nc_country[:15]
squarify.plot(sizes=t.values,label=t.index,color=sns.color_palette("rocket_r", n_colors=15),linewidth=4,text_kwargs={'fontsize':14,'fontweight':'bold'})
plt.title('Top 15 content producing countries')plt.show()Output —
# Movies and Shows Genresdef g_heatmap(df, title):
df['genre'] = df['listed_in'].apply(lambda x : x.replace(' ,',',').replace(', ',',').split(','))
Types = []
for i in df['genre']: Types += i
Types = set(Types)
print("There are {} types".format(len(Types),title))
test = df['genre']
mlb = MultiLabelBinarizer()
res = pd.DataFrame(mlb.fit_transform(test), columns=mlb.classes_, index=test.index)
corr = res.corr()
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
fig, ax = plt.subplots(figsize=(15, 12))
pl = sns.heatmap(corr, mask=mask, cmap=colors1, vmax=.3, vmin=-.3, center=0, square=True, linewidths=2.5)
plt.show()g_heatmap(n_movies, 'Movie')
g_heatmap(n_shows,'Shows')Output —
# Word Cloud of Titlest = str(list(df['title'])).replace(',', '').replace('[', '').replace("'", '').replace(']', '').replace('.', '')wc = WordCloud(background_color = 'white', width = 500, height = 200,colormap='icefire', max_words = 150).generate(t)plt.figure( figsize=(10,10))
plt.imshow(wc, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.title('Word Cloud of Titles on Netflix')plt.show()Output —
# Word Cloud for Castc_df['cast'] = df['cast'].dropna()
t = str(list(c_df['cast'])).replace(',', '').replace('[', '').replace("'", '').replace(']', '').replace('.', '')wc = WordCloud(background_color = 'white', width = 500, height = 200,colormap='icefire', max_words = 150).generate(t)plt.figure( figsize=(10,10))
plt.imshow(wc, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.title('Word Cloud of Cast on Netflix')plt.show()Output —
# Word Cloud for Countryc_df['country'] = df['country'].dropna()
t = str(list(c_df['country'])).replace(',', '').replace('[', '').replace("'", '').replace(']', '').replace('.', '')wc = WordCloud(background_color = 'white', width = 500, height = 200,colormap='icefire', max_words = 150).generate(t)plt.figure( figsize=(10,10))
plt.imshow(wc, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.title('Word Cloud of Country on Netflix')plt.show()Output —
# Number of Released Movies by Yearplt.figure(figsize=(12,10))
sns.countplot(x='release_year',data=n_movies,palette=colors1,order=n_movies['release_year'].value_counts().index[0:15])
plt.title('No of Released Movies by Year')
plt.xlabel('Release Year')
plt.ylabel('Count')
plt.xticks(rotation=45)plt.show()Output —
# Number of Released Shows by Yearplt.figure(figsize=(12,10))
sns.countplot(x='release_year',data=n_shows,palette=colors1,order=n_shows['release_year'].value_counts().index[0:15])
plt.title('No of Released Shows by Year')
plt.xlabel('Release Year')
plt.ylabel('Count')
plt.xticks(rotation=45)plt.show()Output —
That’s it for now. Day 12 : Data Visualization — Part 3!
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Stay Tuned!!
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Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
For Python Projects —
For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Follow for more updates. Stay tuned and keep coding!
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