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CSE 3020: Data Visualization
DA- 1
16 PLOTS FROM A DATASET
PRAVEEN RAJ M
19BCE0685
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
data=pd.read_csv('/content/final.csv')
da = pd.read_csv('/content/final.csv')
1. BARPLOT
age_count=Counter(data.age)
most_common_ages=age_count.most_common(5)
x,y=zip(*most_common_ages)
x,y=list(x),list(y)
# visualization(barplot)
plt.figure(figsize=(10,10))
ax=sns.barplot(x=x,y=y,palette=sns.cubehelix_palette(len(x)))
plt.xlabel('Ages')
plt.ylabel('Frequency')
plt.title('Most Common 5 age of Students')
plt.show()
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2. POINT PLOT
#
Daily alcohol consumption vs First grade of each age
# first grade avarage of each age
age_list=list(data['age'].unique())
first_grades_avarage=[]
for i in age_list:
x=data[data['age']==i]
first_grade_mean=sum(x.G1)/len(x)
first_grades_avarage.append(first_grade_mean)
df=pd.DataFrame({'age_list':age_list,'first_grades_avarage':first_grades_avarage})
new_index=(df['first_grades_avarage'].sort_values(ascending=False)).index.values
sorted_data2=df.reindex(new_index)
# alcohol consumptions avarage of each age
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age_list=list(data['age'].unique())
alcohol_consumptions=[]
for i in age_list:
x=data[data['age']==i]
avarage_alcohol_consumptions=sum(x.Dalc)/len(x)
alcohol_consumptions.append(avarage_alcohol_consumptions)
df=pd.DataFrame({'age_list':age_list,'alcohol_consumptions':alcohol_consumptions})
new_index=(df['alcohol_consumptions'].sort_values(ascending=False)).index.values
sorted_data3=df.reindex(new_index)
# Daily alcohol cunsumptions vs First Grade of
each age
sorted_data2['first_grades_avarage']=sorted_data2['first_grades_avarage']/max(sorted_data2
sorted_data3['alcohol_consumptions']=sorted_data3['alcohol_consumptions']/max(sorted_data3
data=pd.concat([sorted_data2,sorted_data3['alcohol_consumptions']],axis=1)
# visualization(Point Plot)
f,ax=plt.subplots(figsize=(20,10))
sns.pointplot(x='age_list',y='alcohol_consumptions',data=data,color='lime',alpha=0.8)
sns.pointplot(x='age_list',y='first_grades_avarage',data=data,color='red',alpha=0.8)
plt.text(6,0.6,'alcohol consumptions',color='red',fontsize=17,style='italic')
plt.text(6,0.55,'first grades avarage',color='lime',fontsize=18,style='italic')
plt.xlabel('Ages',fontsize=15,color='blue')
plt.ylabel('Values',fontsize=15,color='blue')
plt.title('Daily alcohol consumptions vs first Grade of each age ',fontsize=20,color='blue
plt.grid()
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3.JOINT PLOT(KDE)
# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
# visualization(joint plot)
g=sns.jointplot(data.first_grades_avarage,data.alcohol_consumptions,kind='kde',height=7)
plt.savefig('graph.png')
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pas
FutureWarning
4.JOINT PLOT
# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
g=sns.jointplot('first_grades_avarage','alcohol_consumptions',data=data,height=5,ratio=3,c
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/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pas
FutureWarning
5.PIE CHART
# Health rates according in data
labels=da.health.value_counts().index
colors=['grey','blue','red','yellow','green']
explode=[0,0,0,0,0]
sizes=da.health.value_counts().values
# visualization(Pie plot)
plt.figure(figsize=(7,7))
plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%')
plt.title('Health Rates According in data',color='blue',fontsize=15)
plt.show()
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6.KDE PLOT
# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
# visualization(kdeplot)
sns.kdeplot(data.first_grades_avarage,data.alcohol_consumptions,shade=True,cut=5)
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pas
FutureWarning
7.LM PLOT
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
# visualization(lm-plot)
sns.lmplot(x='first_grades_avarage',y='alcohol_consumptions',data=data)
plt.show()
8.HEAT MAP
# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
# visualization(Seaborn-Heatmap)
f,ax=plt.subplots(figsize=(5,5))
sns.heatmap(data.corr(),annot=True,linewidths=.5,linecolor='r',fmt='.1f',ax=ax)
plt.show()
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9.BOX PLOT
# Box plot
# sex
# first grade
# reason
sns.boxplot(x='sex',y='G1',hue='reason',data=da,palette='PRGn')
plt.show()
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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10. SWARM PLOT
# Swarm plot
# visualization(Seaborn-Swarm plot)
sns.swarmplot(x='sex',y='G1',hue='Pstatus',data=da)
plt.show()
/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 49.
warnings.warn(msg, UserWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 36.
warnings.warn(msg, UserWarning)
11. PAIR PLOT
# Visualization of Daily alcohol cunsumptions vs First Grade of each ages with different s
# Visualization(Seaborn-Pair Plot)
data.drop(['age_list'],axis=1,inplace=True)
sns.pairplot(data)
plt.show()
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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12. COUNT PLOT
# Count plot
# sex
sns.countplot(da.age)
plt.title('age',color='blue',fontsize=15)
plt.show()
# school
sns.countplot(da.school)
plt.title('school',color='blue',fontsize=15)
plt.show()
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/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pas
FutureWarning
/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pas
FutureWarning
13. SCATTER PLOT
#Scatter Plot between G2 and G3 based on Mothers Job as category
categories = np.unique(da['Mjob'])
colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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# Draw Plot for Each Category
plt.figure(figsize=(6, 6), dpi= 80, facecolor='w', edgecolor='k')
for i, category in enumerate(categories):
plt.scatter('G2', 'G3',
data=da.loc[da.Mjob==category, :],
s=20, c=colors[i], label=str(category))
# Decorations
plt.gca().set(xlim=(0,22), ylim=(0, 22),
xlabel='Free Time', ylabel='Age')
plt.xticks(fontsize=12); plt.yticks(fontsize=12)
plt.title("Scatterplot of Free Time vs Going Out", fontsize=13)
plt.legend(fontsize=12)
plt.show()
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14. VIOLIN PLOT
#Violin Plot of G2 scores by Daily Alcohol
# Draw Plot
plt.figure(figsize=(5,5), dpi= 80)
sns violinplot(x='Dalc' y='G2' data=da scale='width'
inner='quartile')
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sns.violinplot(x= Dalc , y= G2 , data=da, scale= width , inner= quartile )
# Decoration
plt.title('Violin Plot of G2 scores by Daily Alcohol', fontsize=13)
plt.show()
15. TREEMAP
pip install squarify
Collecting squarify
Downloading https://files.pythonhosted.org/packages/0b/2b/2e77c35326efec19819cd1d7
Installing collected packages: squarify
Successfully installed squarify-0.4.3
#Treemap of Health values
import squarify
labels=da.health.value_counts().index
colors = [plt.cm.Spectral(i/float(len(labels))) for i in range(len(labels))]
explode=[0,0,0,0,0]
sizes=da.health.value_counts().values
# Draw Plot
plt.figure(figsize=(6,6), dpi= 80)
squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8)
# Decorate
plt.title('Treemap of Health values')
plt.axis('off')
plt.show()
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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16. STACKED HISTOGRAM
#Stacked Histogram of Weekly Alcohol colored by Family Relation Score
# Prepare data
x_var = 'Dalc'
groupby_var = 'age'
df_agg = da.loc[:, [x_var, groupby_var]].groupby(groupby_var)
vals = [da[x_var].values.tolist() for i, da in df_agg]
# Draw
plt.figure(figsize=(8,8), dpi= 80)
colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))]
n, bins, patches = plt.hist(vals, da[x_var].unique().__len__(), stacked=True, density=Fals
# Decoration
plt.legend({group:col for group, col in zip(np.unique(da[groupby_var]).tolist(), colors[:l
plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=13)
plt.xlabel(x_var)
plt.ylabel("Frequency")
plt.ylim(0, 750)
plt.xticks(ticks=bins, labels=np.unique(da[x_var]).tolist(), rotation=90, horizontalalignm
plt.show()
https://colab.research.google.com/drive/1WXteMZC2qGWuVMPcC8lnm7_YTo2S_54-#scrollTo=Rzny3nzWlMGN&printMode=true
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/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecation
return array(a, dtype, copy=False, order=order)
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