# this is the convention to import matplotlib import matplotlib.pyplot as plt # we need numpy to generate info import numpy as np

# get info x = np.linspace(0,5,11) y = x**2

plt.plot(x,y) plt.show()

# we can add attributes to modify the graphic plt.plot(x,y,'ys-') # attribute 'ys-' means: Show me yellow line (y) with squares united (s-) plt.show()

plt.hist(x) plt.show()

plt.pie(x) plt.show()

plt.scatter(x,y) plt.show()

plt.boxplot(x) plt.show()

plt.subplot(1,2,1) # (#rows, #colums, #index) - Graphic 1 plt.plot(x,y) plt.subplot(1,2,2) # - Graphic 2 plt.hist(x) plt.show()

plt.subplot(1,2,1) plt.plot(x,y, 'r--') # Plot 1 plt.plot(y,x, 'b:') # Plot 2 plt.subplot(1,2,2) # Graphic 2 plt.pie(y) plt.show()

# axes parameters: [pos axis x, pos axis y, size graph axis x, size graph axis y] fig = plt.figure() # Object axes = fig.add_axes([0.1, 0.1, 0.8, 0.9]) # Object graphic 1 axes2 = fig.add_axes([0.17, 0.55, 0.4, 0.3]) # Object graphic 2 axes.plot(x,y,'b') # Plot 1 axes2.plot(y,x,'r') # Plot 2 axes2.set_facecolor('gray') fig.show()

# Generate a objects with 2 rows and 4 colums. In total 8 objects fig, axes = plt.subplots(2,4) # Each object we can access through the matrix index axes[0,0].plot(x,np.cos(x)*-1) axes[0,1].plot(x,np.sin(x)*-1, 'r') axes[0,2].plot(x,np.tan(x)*-1, 'g') axes[0,3].plot(x,np.cos(x)*-1) axes[1,0].plot(x,np.cos(x)) axes[1,1].plot(x,np.sin(x), 'r') axes[1,2].plot(x,np.tan(x), 'g') axes[1,3].plot(x,np.cos(x)**2) # Improved visualization of the axes of each chart fig.tight_layout()

plt.plot(np.sin(x),y, label='sin(x)') plt.title('This is a title') plt.xlabel('Axis x') plt.ylabel('Axis y') plt.legend(loc='lower left') # It is necessary to add this line for our label to appear. plt.show()

fig, axes = plt.subplots(1,2,figsize=(5,5)) # Object 1 axes[0].plot(np.sin(x),y,label='$sin(x)$') axes[0].set_title('Title object 1') axes[0].set_xlabel('Axis x Object 1') axes[0].set_ylabel('Axis y Object 1') axes[0].legend() # It is necessary to add this line for our label to appear. #Object 2 axes[1].plot(y,np.sin(x),label='Relation y-sin(x)') axes[1].set_title('Title object 2') axes[1].set_xlabel('Axis x Object 2') axes[1].set_ylabel('Axis y Object 2') axes[1].legend(loc='lower right') fig.tight_layout()

# Show us all styles that Matplotlib has print(plt.style.available)

# Line to define style that we will use in ours graphics plt.style.use('dark_background')

fig, ax = plt.subplots(figsize=(7,7)) ax.plot(x,x+1,'r') fig.tight_layout()

fig, ax = plt.subplots(figsize=(7,7)) ax.plot(x,x+1,'r--') ax.plot(x,x+2,'b-') ax.plot(x,x+3,'y.-') ax.plot(x,x+4,'go:') fig.tight_layout()

fig, ax = plt.subplots(figsize=(7,7)) ax.plot(x,x+1,color='#D426C8', alpha=0.2, linewidth=2, linestyle='--', marker='x') # RGB colors ax.plot(x,x+2,color='#66FF89',linewidth=8, linestyle='-', marker='o', markersize=7) ax.plot(x,x+3,color='blue',linewidth=6, linestyle='dashed', marker='v') # Comun colors ax.plot(x,x+4,color='white',linewidth=4, linestyle=':', marker='P', markerfacecolor='blue') fig.tight_layout()

# Get data countrys = ['Germany', 'Switzerland', 'Greece', 'Italy', 'England'] population = [1000,800,900,1000,300]

# Line to define style that we will use in ours graphics plt.style.use('default')

plt.bar(countrys, population, width=0.5, color=['r', 'b']) plt.xticks(rotation=45) plt.title('Country Population') plt.xlabel('Countrys') plt.ylabel('Population') plt.show()

plt.barh(countrys, population, color=['r', 'b','y','c']) plt.xticks(rotation=45) plt.title('Country Population') plt.xlabel('Countrys') plt.ylabel('Population') plt.show()

# Get data data = np.random.normal(5,10,100)

plt.hist(data, bins=7) # bins = bars numbers plt.show()

plt.hist(data, bins=6, histtype='step') # bins = bars numbers plt.show()

plt.boxplot(data, vert=False, patch_artist=True, notch=True, showfliers=False) plt.show()

# Get data N=50 x = np.random.rand(N) y = np.random.rand(N) area = (30 * np.random.rand(N)) **2 colors = np.random.rand(N)

plt.scatter(x,y,s=area, c=colors, alpha=0.5) plt.show()

# this is the convention to import seaborn import seaborn as sns

sns.set(style='dark', palette='dark', font="Verdana", font_scale=1) sns.barplot(x=['A','B','C'], y=[1,3,2]) plt.show() # seaborn is built with matplotlib. Some matplotlib commands affect seaborn, for example plt.show()

tips = sns.load_dataset('tips') # Dataset from seaborn

sns.displot(data= tips, x= 'total_bill') plt.show()

# The hue attribute allows us to group with other variable sns.displot(data= tips, x='total_bill', y='tip', hue ='sex') plt.show()

# legend: dont show us hue segmentation # palette: colors # alpha: transparency sns.displot(data= tips, x='total_bill', hue ='sex', kind ='kde', legend= False, palette='dark', alpha =.5) plt.show()

# Chart for distributions and accumulation sns.histplot(data=tips, x='tip', bins =15, cumulative=True) plt.show()

# As shown on the y-axis and stat, present the multiple data # multiple has other types: stack, layer and fill sns.histplot(data=tips, x='tip', bins=15, hue='sex', stat='density', multiple='dodge') plt.show()

# kde graphs: density graph sns.kdeplot(data=tips, x='tip', hue='sex') plt.show()

# with accumulation sns.kdeplot(data=tips, x='tip', hue='sex', cumulative=True) plt.show()

# shade: Create an area under the curve sns.kdeplot(data=tips, x='tip', hue='sex', shade=True) plt.show()

# displot: distribution graphs # kind: allow us change graphs sns.displot(data=tips, x='tip', hue='sex',kind='hist', multiple='stack') plt.show()

# countplot: variables are taken into account sns.countplot(data=tips, x='day', hue='sex'); # with "a" in the final line doesnt need plt.show()

sns.swarmplot(data=tips, x='day', y ='total_bill', hue='sex', dodge=True);

sns.boxplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True);

# Violin: similar to Boxplot, but does not show the quartiles, but how the data is concentrated with split comparison. sns.violinplot(data=tips, x='day', y='total_bill', hue='sex', split=True, palette='pastel');

# kind: allow us change graph # col: allow us add new variable sns.catplot(data=tips, x='day', y='total_bill', hue='sex', dodge=True, kind='box', col='time');

# scatter sns.scatterplot(data= tips, x= 'total_bill', y = 'tip', hue= 'day');

sns.scatterplot(data=tips, x='total_bill', y ='tip', hue='day', style='time', size='size') plt.legend(loc='center', bbox_to_anchor=(1.12, 0.5)) plt.show()

markers = {"Lunch": 'D', "Dinner": 's'} sns.scatterplot(data=tips, x='total_bill', y ='tip', hue='day', style='time', size='size', markers=markers); plt.legend(loc='center', bbox_to_anchor=(1.12, 0.5));

# Line sns.lineplot(data=tips, x='total_bill', y ='tip', hue='day', style='time', size='size'); plt.legend(loc='center', bbox_to_anchor=(1.12, 0.5));

sns.relplot(data=tips, x='total_bill', y='tip', hue='day', style='time', size='size', kind='scatter', col='time');

# jointplot: joins 2 different graphs into one sns.jointplot(data=tips, x='total_bill', y='tip', hue ='sex');

# more custom sns.jointplot(data=tips, x='total_bill', y='tip', hue='sex', kind='hist', marginal_ticks=True, # shows a small table for the external graphic marginal_kws=dict(bins=25, fill =True, multiple='dodge')); # marginal_kws: different parameters can be added to it

# Pairplot: correlates all the numeric variables in the dataset sns.pairplot(data= tips, hue='sex');

# corner: show us one to one relation sns.pairplot(data= tips, hue='sex', corner=True);

# Correlation between variables tips.corr()

# Heatmap sns.heatmap(tips.corr());

# annot: show us value sns.heatmap(tips.corr(), annot=True, linewidths=5);