Bar plots.ipynb colaboratory
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The document contains code for creating various bar plots and visualizations using Python libraries like Matplotlib, Seaborn, and Plotly. It includes bar plots showing student enrollment data by course, total bill by time of day from a sample dataset, Canadian population data over time, FIFA player ratings over multiple years, top 10 countries by total medals, top 10 athletes by number of medals awarded, sports with most medals, analysis of medals won by gender and discipline over time. Stacked bar plots and grouped bar plots are also created to show comparisons between categories in the datasets.
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import plotly.express as px
data_canada = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(data_canada, x='year', y='pop')
fig.show()
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import pandas as pd
plotdata = pd.DataFrame({
"2018":[57,67,77,83],
"2019":[68,73,80,79],
"2020":[73,78,80,85]},
index=["Django", "Gafur", "Tommy", "Ronnie"])
plotdata.plot(kind="bar",figsize=(15, 8))
plt.title("FIFA ratings")
plt.xlabel("Footballer")
plt.ylabel("Ratings")](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-2-320.jpg)
![Text(0, 0.5, 'Ratings')
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import pandas as pd
plotdata = pd.DataFrame({
"2018":[57,67,77,83],
"2019":[68,73,80,79],
"2020":[73,78,80,85]},
index=["Django", "Gafur", "Tommy", "Ronnie"])
plotdata.plot(kind='bar', stacked=True,figsize=(15, 8))](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-3-320.jpg)
![<matplotlib.axes._subplots.AxesSubplot at 0x7fb62ed80b50>
Text(0, 0.5, 'Ratings')
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import pandas as pd
plotdata = pd.DataFrame({
"2018":[57,67,77,83],
"2019":[68,73,80,79],
"2020":[73,78,80,85]},
index=["Django", "Gafur", "Tommy", "Ronnie"])
plotdata.plot(kind='bar', stacked=True,figsize=(15, 8))
plt.title("FIFA ratings")
plt.xlabel("Footballer")
plt.ylabel("Ratings")
1 df=pd.read_csv("Summer-Olympic-medals-1976-to-2008.csv",encoding='latin-1')
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top_10.plot(kind='bar',figsize=(10,8))
plt.title('All Time Medals of top 10 countries')
top_10 = df['Country'].value_counts()[:10]](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-4-320.jpg)
![Text(0.5, 1.0, 'All Time Medals of top 10 countries')
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indpie = df[df['Country']=='United States']['Medal'].value_counts()
indpie.plot(kind='bar',figsize=(10,8))](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-5-320.jpg)
![<matplotlib.axes._subplots.AxesSubplot at 0x7fb62e63ccd0>
/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:
Pass the following variable as a keyword arg: x. From version 0.12, the only valid p
Text(0, 0.5, 'No. of Athlete')
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plt.figure(figsize=(10, 5))
sns.countplot(df['Year'])
plt.title('Total Athletes contribution in summer olympics over time')
plt.xlabel('Years')
plt.ylabel('No. of Athlete')
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athlete_order = df['Athlete'].value_counts().head(10).index
sns.countplot(data=df, y='Athlete', order=athlete_order)
plt.title('Top 10 Athletes with the most awarded Medals')
plt.xlabel('No. of awrded medals')
plt.figure(figsize=(9, 5))
plt.ylabel('Athlete Name');](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-6-320.jpg)
![Text(0, 0.5, 'No. of Medals')
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plt.figure(figsize=(15, 5))
highest_sport = df['Sport'].value_counts().index
sns.countplot(data=df, x='Sport', order=highest_sport)
plt.xticks(rotation=75)
plt.title('Sports with most awarded Medals')
plt.xlabel('Sport')
plt.ylabel('No. of Medals')
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sns.countplot(x='Year',hue='Medal',data=df)
sns.set(rc={'figure.figsize':(10,10)})
plt.title("Type of medals won over the years")](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-7-320.jpg)
![Text(0.5, 1.0, 'Type of medals won over the years')
<matplotlib.axes._subplots.AxesSubplot at 0x7fb62e50efd0>
1 sns.countplot(x="Medal", hue="Gender", data=df)
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gender_group = df.groupby(['Year', 'Gender']).size().unstack()
gender_group.apply(lambda x:x/x.sum(), axis=1).plot(kind='barh', stacked=True, legend
plt.legend(['Men', 'Women'], bbox_to_anchor=(1.0, 0.7))
plt.xlabel('Men / Women ratio')](https://image.slidesharecdn.com/barplots-210928174007/85/Bar-plots-ipynb-colaboratory-8-320.jpg)
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Bar plots.ipynb colaboratory
- 4. <matplotlib.axes._subplots.AxesSubplot at 0x7fb62ed80b50> Text(0, 0.5, 'Ratings') 1 2 3 4 5 6 7 8 9 10 11 import pandas as pd plotdata = pd.DataFrame({ "2018":[57,67,77,83], "2019":[68,73,80,79], "2020":[73,78,80,85]}, index=["Django", "Gafur", "Tommy", "Ronnie"]) plotdata.plot(kind='bar', stacked=True,figsize=(15, 8)) plt.title("FIFA ratings") plt.xlabel("Footballer") plt.ylabel("Ratings") 1 df=pd.read_csv("Summer-Olympic-medals-1976-to-2008.csv",encoding='latin-1') 1 3 4 2 5 top_10.plot(kind='bar',figsize=(10,8)) plt.title('All Time Medals of top 10 countries') top_10 = df['Country'].value_counts()[:10]
- 5. Text(0.5, 1.0, 'All Time Medals of top 10 countries') 1 2 3 indpie = df[df['Country']=='United States']['Medal'].value_counts() indpie.plot(kind='bar',figsize=(10,8))
- 6. <matplotlib.axes._subplots.AxesSubplot at 0x7fb62e63ccd0> /usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid p Text(0, 0.5, 'No. of Athlete') 1 2 3 4 5 plt.figure(figsize=(10, 5)) sns.countplot(df['Year']) plt.title('Total Athletes contribution in summer olympics over time') plt.xlabel('Years') plt.ylabel('No. of Athlete') 1 3 4 5 2 6 athlete_order = df['Athlete'].value_counts().head(10).index sns.countplot(data=df, y='Athlete', order=athlete_order) plt.title('Top 10 Athletes with the most awarded Medals') plt.xlabel('No. of awrded medals') plt.figure(figsize=(9, 5)) plt.ylabel('Athlete Name');
- 7. Text(0, 0.5, 'No. of Medals') 1 2 3 4 5 6 7 plt.figure(figsize=(15, 5)) highest_sport = df['Sport'].value_counts().index sns.countplot(data=df, x='Sport', order=highest_sport) plt.xticks(rotation=75) plt.title('Sports with most awarded Medals') plt.xlabel('Sport') plt.ylabel('No. of Medals') 1 2 3 sns.countplot(x='Year',hue='Medal',data=df) sns.set(rc={'figure.figsize':(10,10)}) plt.title("Type of medals won over the years")
- 8. Text(0.5, 1.0, 'Type of medals won over the years') <matplotlib.axes._subplots.AxesSubplot at 0x7fb62e50efd0> 1 sns.countplot(x="Medal", hue="Gender", data=df) 1 2 3 4 5 6 7 gender_group = df.groupby(['Year', 'Gender']).size().unstack() gender_group.apply(lambda x:x/x.sum(), axis=1).plot(kind='barh', stacked=True, legend plt.legend(['Men', 'Women'], bbox_to_anchor=(1.0, 0.7)) plt.xlabel('Men / Women ratio')
- 9. Text(0.5, 0, 'Men / Women ratio') 1 2 3 4 5 sns.countplot(y='Discipline',hue='Gender',data=df) sns.set(rc={'figure.figsize':(10,10)}) plt.xticks(rotation=90) plt.title('Medals by Gender in each Discipline') plt.legend(loc=1) # 1 is code for 'upper right'3
- 10. check 0s completed at 11:52 PM <matplotlib.legend.Legend at 0x7fb62e19afd0> <seaborn.axisgrid.FacetGrid at 0x7fb62dfd3210> 1 sns.catplot(x="Medal", y="Year", hue="Gender",kind="box", data=df)