Youtor
Visualize Plot and Subplots using Matplotlib and Python P1
YOUR LINK HERE:
http://youtube.com/watch?v=PQ31FTpVSJU
Visualize Plot and Subplots using Matplotlib and Python • Topic to be Covered - Data Visualisation • Link for the Dataset - http://www.randalolson.com/2014/06/14... • import pandas as pd • import matplotlib.pyplot as plt • df = pd.read_csv('percent-bachelors-degrees-women-usa.csv') • No 1 • plt.plot(df['Year'],df['Agriculture'],color='red') • plt.plot(df['Year'],df['Architecture'],color='blue') • plt.plot(df['Year'],df['Art and Performance'],color='green') • plt.plot(df['Year'],df['Physical Sciences'],color='red') • plt.plot(df['Year'],df['Computer Science'],color='blue') • No 2 • ''' • 1 2 3 • 1 x 3 • 1,3,1 • 1,3,2 • 1,3,3''' • plt.subplot(1,3,1) • plt.plot(df['Year'],df['Architecture'],color='blue') • plt.title('Architecture') • plt.subplot(1,3,2) • plt.plot(df['Year'],df['Computer Science'],color='green') • plt.title('Computer Science') • plt.subplot(1,3,3) • plt.plot(df['Year'],df['Physical Sciences'],color='yellow') • plt.title('Physical Sciences') • plt.show() • No 3 • plt.plot(df['Year'], 100 -df['English'],c='blue',label='Men') • plt.plot(df['Year'], df['English'],c='red',label='Women') • plt.title('English Enrollment Comparion between the gender') • plt.xlabel('Year') • plt.ylabel('Enrollment in percentage') • No 4 • ''' • 1 2 3 • 4 5 6 • 7 8 9 • 10 11 12 • 13 14 15 • 16 17 • 6 x 3''' • fig = plt.figure(figsize=(13,5)) • ax1 = fig.add_subplot(6,3,1) • ax2 = fig.add_subplot(6,3,2) • ax3 = fig.add_subplot(6,3,3) • ax4 = fig.add_subplot(6,3,4) • ax5 = fig.add_subplot(6,3,5) • ax6 = fig.add_subplot(6,3,6) • ax7 = fig.add_subplot(6,3,7) • ax8 = fig.add_subplot(6,3,8) • ax9 = fig.add_subplot(6,3,9) • ax10 = fig.add_subplot(6,3,10) • ax11 = fig.add_subplot(6,3,11) • ax12 = fig.add_subplot(6,3,12) • ax13 = fig.add_subplot(6,3,13) • ax14 = fig.add_subplot(6,3,14) • ax15 = fig.add_subplot(6,3,15) • ax16 = fig.add_subplot(6,3,16) • ax17 = fig.add_subplot(6,3,17) • categories = ['Agriculture','Architecture','Art and Performance', • 'Biology','Business','Communications and Journalism', • 'Computer Science','Education','Engineering', • 'English','Foreign Languages','Health Professions', • 'Math and Statistics','Physical Sciences','Psychology', • 'Public Administration','Social Sciences and History'] • ax = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9,ax10,ax11,ax12,ax13,ax14,ax15,ax16,ax17] • for i in range(len(categories)): • ax[i].plot(df['Year'],df[categories[i]],c='red',label='Women') • ax[i].plot(df['Year'],100-df[categories[i]],c='blue',label='Women') • ax[i].set_title(categories[i]) • ax[i].set_ylim(0,100) • • plt.tight_layout() • plt.savefig('categories.jpeg') • plt.show() • All Playlist of this youtube channel • ======= • 1. Data Preprocessing in Machine Learning • • Data Preprocessing in Machine Learnin... • 2. Confusion Matrix in Machine Learning, ML, AI • • Confusion Matrix in Machine Learning,... • 3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz • • Anaconda | Python Installation | Spyd... • 4. Cross Validation, Sampling, train test split in Machine Learning • • Cross Validation | Sampling | train t... • 5. Drop and Delete Operations in Python Pandas • • Drop and Delete Operations in Python ... • 6. Matrices and Vectors with python • • Matrices and Vectors with python • 7. Detect Outliers in Machine Learning • • Detect Outliers in Machine Learning • 8. TimeSeries preprocessing in Machine Learning • • TimeSeries preprocessing in Machine L... • 9. Handling Missing Values in Machine Learning • • Handling Missing Values in Machine Le... • 10. Dummy Encoding Encoding in Machine Learning • • Label Encoding, One hot Encoding, Dum... • 11. Data Visualisation with Python, Seaborn, Matplotlib • • Data Visualisation with Python, Matpl... • 12. Feature Scaling in Machine Learning • • Feature Scaling in Machine Learning • 13. Python 3 basics for Beginner • • Python | Python 3 Basics | Python for... • 14. Statistics with Python • • Statistics with Python • 15. Sklearn Scikit Learn Machine Learning • • Sklearn Scikit Learn Machine Learning • 16. Python Pandas Dataframe Operations • • Python Pandas Dataframe Operations • 17. Linear Regression, Supervised Machine Learning • • Linear Regression | Supervised Machin... • 18 Interiew Questions on Machine Learning and Data Science • • Interview Question for Machine Learni... • 19. Jupyter Notebook Operations • • Jupyter and Spyder Notebook Operation...
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