50 Matplotlib-Schattierungen - The Master Plots (mit vollständigem Python-Code)

 # Import dataset midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") # Prepare Data # Create as many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))] # Draw Plot for Each Category plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k') for i, category in enumerate(categories): plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :], s=20, c=colors[i], label=str(category)) # Decorations plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000), xlabel='Area', ylabel='Population') plt.xticks(fontsize=12); plt.yticks(fontsize=12) plt.title("Scatterplot of Midwest Area vs Population", fontsize=22) plt.legend(fontsize=12) plt.show() 

 from matplotlib import patches from scipy.spatial import ConvexHull import warnings; warnings.simplefilter('ignore') sns.set_style("white") # Step 1: Prepare Data midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") # As many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k') for i, category in enumerate(categories): plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :], s='dot_size', c=colors[i], label=str(category), edgecolors='black', linewidths=.5) # Step 3: Encircling # https://stackoverflow.com/questions/44575681/how-do-i-encircle-different-data-sets-in-scatter-plot def encircle(x,y, ax=None, **kw): if not ax: ax=plt.gca() p = np.c_[x,y] hull = ConvexHull(p) poly = plt.Polygon(p[hull.vertices,:], **kw) ax.add_patch(poly) # Select data to be encircled midwest_encircle_data = midwest.loc[midwest.state=='IN', :] # Draw polygon surrounding vertices encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="k", fc="gold", alpha=0.1) encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="firebrick", fc="none", linewidth=1.5) # Step 4: Decorations plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000), xlabel='Area', ylabel='Population') plt.xticks(fontsize=12); plt.yticks(fontsize=12) plt.title("Bubble Plot with Encircling", fontsize=22) plt.legend(fontsize=12) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_select = df.loc[df.cyl.isin([4,8]), :] # Plot sns.set_style("white") gridobj = sns.lmplot(x="displ", y="hwy", hue="cyl", data=df_select, height=7, aspect=1.6, robust=True, palette='tab10', scatter_kws=dict(s=60, linewidths=.7, edgecolors='black')) # Decorations gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50)) plt.title("Scatterplot with line of best fit grouped by number of cylinders", fontsize=20) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_select = df.loc[df.cyl.isin([4,8]), :] # Each line in its own column sns.set_style("white") gridobj = sns.lmplot(x="displ", y="hwy", data=df_select, height=7, robust=True, palette='Set1', col="cyl", scatter_kws=dict(s=60, linewidths=.7, edgecolors='black')) # Decorations gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50)) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") # Draw Stripplot fig, ax = plt.subplots(figsize=(16,10), dpi= 80) sns.stripplot(df.cty, df.hwy, jitter=0.25, size=8, ax=ax, linewidth=.5) # Decorations plt.title('Use jittered plots to avoid overlapping of points', fontsize=22) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_counts = df.groupby(['hwy', 'cty']).size().reset_index(name='counts') # Draw Stripplot fig, ax = plt.subplots(figsize=(16,10), dpi= 80) sns.scatterplot(df_counts.cty, df_counts.hwy, size=df_counts.counts*2, ax=ax) # Decorations plt.title('Counts Plot - Size of circle is bigger as more points overlap', fontsize=22) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") # Create Fig and gridspec fig = plt.figure(figsize=(16, 10), dpi= 80) grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2) # Define the axes ax_main = fig.add_subplot(grid[:-1, :-1]) ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[]) ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[]) # Scatterplot on main ax ax_main.scatter('displ', 'hwy', s=df.cty*4, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="tab10", edgecolors='gray', linewidths=.5) # histogram on the right ax_bottom.hist(df.displ, 40, histtype='stepfilled', orientation='vertical', color='deeppink') ax_bottom.invert_yaxis() # histogram in the bottom ax_right.hist(df.hwy, 40, histtype='stepfilled', orientation='horizontal', color='deeppink') # Decorations ax_main.set(title='Scatterplot with Histograms \n displ vs hwy', xlabel='displ', ylabel='hwy') ax_main.title.set_fontsize(20) for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()): item.set_fontsize(14) xlabels = ax_main.get_xticks().tolist() ax_main.set_xticklabels(xlabels) plt.show() 

 # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") # Create Fig and gridspec fig = plt.figure(figsize=(16, 10), dpi= 80) grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2) # Define the axes ax_main = fig.add_subplot(grid[:-1, :-1]) ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[]) ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[]) # Scatterplot on main ax ax_main.scatter('displ', 'hwy', s=df.cty*5, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="Set1", edgecolors='black', linewidths=.5) # Add a graph in each part sns.boxplot(df.hwy, ax=ax_right, orient="v") sns.boxplot(df.displ, ax=ax_bottom, orient="h") # Decorations ------------------ # Remove x axis name for the boxplot ax_bottom.set(xlabel='') ax_right.set(ylabel='') # Main Title, Xlabel and YLabel ax_main.set(title='Scatterplot with Histograms \n displ vs hwy', xlabel='displ', ylabel='hwy') # Set font size of different components ax_main.title.set_fontsize(20) for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()): item.set_fontsize(14) plt.show() 

 # Import Dataset df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") # Plot plt.figure(figsize=(12,10), dpi= 80) sns.heatmap(df.corr(), xticklabels=df.corr().columns, yticklabels=df.corr().columns, cmap='RdYlGn', center=0, annot=True) # Decorations plt.title('Correlogram of mtcars', fontsize=22) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.show() 

 # Load Dataset df = sns.load_dataset('iris') # Plot plt.figure(figsize=(10,8), dpi= 80) sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5)) plt.show() 

 # Load Dataset df = sns.load_dataset('iris') # Plot plt.figure(figsize=(10,8), dpi= 80) sns.pairplot(df, kind="reg", hue="species") plt.show() 

 # Prepare Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") x = df.loc[:, ['mpg']] df['mpg_z'] = (x - x.mean())/x.std() df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']] df.sort_values('mpg_z', inplace=True) df.reset_index(inplace=True) # Draw plot plt.figure(figsize=(14,10), dpi= 80) plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=5) # Decorations plt.gca().set(ylabel='$Model$', xlabel='$Mileage$') plt.yticks(df.index, df.cars, fontsize=12) plt.title('Diverging Bars of Car Mileage', fontdict={'size':20}) plt.grid(linestyle='--', alpha=0.5) plt.show() 

 # Prepare Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") x = df.loc[:, ['mpg']] df['mpg_z'] = (x - x.mean())/x.std() df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']] df.sort_values('mpg_z', inplace=True) df.reset_index(inplace=True) # Draw plot plt.figure(figsize=(14,14), dpi= 80) plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z) for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z): t = plt.text(x, y, round(tex, 2), horizontalalignment='right' if x < 0 else 'left', verticalalignment='center', fontdict={'color':'red' if x < 0 else 'green', 'size':14}) # Decorations plt.yticks(df.index, df.cars, fontsize=12) plt.title('Diverging Text Bars of Car Mileage', fontdict={'size':20}) plt.grid(linestyle='--', alpha=0.5) plt.xlim(-2.5, 2.5) plt.show() 

 # Prepare Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") x = df.loc[:, ['mpg']] df['mpg_z'] = (x - x.mean())/x.std() df['colors'] = ['red' if x < 0 else 'darkgreen' for x in df['mpg_z']] df.sort_values('mpg_z', inplace=True) df.reset_index(inplace=True) # Draw plot plt.figure(figsize=(14,16), dpi= 80) plt.scatter(df.mpg_z, df.index, s=450, alpha=.6, color=df.colors) for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z): t = plt.text(x, y, round(tex, 1), horizontalalignment='center', verticalalignment='center', fontdict={'color':'white'}) # Decorations # Lighten borders plt.gca().spines["top"].set_alpha(.3) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(.3) plt.gca().spines["left"].set_alpha(.3) plt.yticks(df.index, df.cars) plt.title('Diverging Dotplot of Car Mileage', fontdict={'size':20}) plt.xlabel('$Mileage$') plt.grid(linestyle='--', alpha=0.5) plt.xlim(-2.5, 2.5) plt.show() 

 # Prepare Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") x = df.loc[:, ['mpg']] df['mpg_z'] = (x - x.mean())/x.std() df['colors'] = 'black' # color fiat differently df.loc[df.cars == 'Fiat X1-9', 'colors'] = 'darkorange' df.sort_values('mpg_z', inplace=True) df.reset_index(inplace=True) # Draw plot import matplotlib.patches as patches plt.figure(figsize=(14,16), dpi= 80) plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=1) plt.scatter(df.mpg_z, df.index, color=df.colors, s=[600 if x == 'Fiat X1-9' else 300 for x in df.cars], alpha=0.6) plt.yticks(df.index, df.cars) plt.xticks(fontsize=12) # Annotate plt.annotate('Mercedes Models', xy=(0.0, 11.0), xytext=(1.0, 11), xycoords='data', fontsize=15, ha='center', va='center', bbox=dict(boxstyle='square', fc='firebrick'), arrowprops=dict(arrowstyle='-[, widthB=2.0, lengthB=1.5', lw=2.0, color='steelblue'), color='white') # Add Patches p1 = patches.Rectangle((-2.0, -1), width=.3, height=3, alpha=.2, facecolor='red') p2 = patches.Rectangle((1.5, 27), width=.8, height=5, alpha=.2, facecolor='green') plt.gca().add_patch(p1) plt.gca().add_patch(p2) # Decorate plt.title('Diverging Bars of Car Mileage', fontdict={'size':20}) plt.grid(linestyle='--', alpha=0.5) plt.show() 

 import numpy as np import pandas as pd # Prepare Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv", parse_dates=['date']).head(100) x = np.arange(df.shape[0]) y_returns = (df.psavert.diff().fillna(0)/df.psavert.shift(1)).fillna(0) * 100 # Plot plt.figure(figsize=(16,10), dpi= 80) plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] >= 0, facecolor='green', interpolate=True, alpha=0.7) plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] <= 0, facecolor='red', interpolate=True, alpha=0.7) # Annotate plt.annotate('Peak \n1975', xy=(94.0, 21.0), xytext=(88.0, 28), bbox=dict(boxstyle='square', fc='firebrick'), arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white') # Decorations xtickvals = [str(m)[:3].upper()+"-"+str(y) for y,m in zip(df.date.dt.year, df.date.dt.month_name())] plt.gca().set_xticks(x[::6]) plt.gca().set_xticklabels(xtickvals[::6], rotation=90, fontdict={'horizontalalignment': 'center', 'verticalalignment': 'center_baseline'}) plt.ylim(-35,35) plt.xlim(1,100) plt.title("Month Economics Return %", fontsize=22) plt.ylabel('Monthly returns %') plt.grid(alpha=0.5) plt.show() 

 # Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean()) df.sort_values('cty', inplace=True) df.reset_index(inplace=True) # Draw plot import matplotlib.patches as patches fig, ax = plt.subplots(figsize=(16,10), facecolor='white', dpi= 80) ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=20) # Annotate Text for i, cty in enumerate(df.cty): ax.text(i, cty+0.5, round(cty, 1), horizontalalignment='center') # Title, Label, Ticks and Ylim ax.set_title('Bar Chart for Highway Mileage', fontdict={'size':22}) ax.set(ylabel='Miles Per Gallon', ylim=(0, 30)) plt.xticks(df.index, df.manufacturer.str.upper(), rotation=60, horizontalalignment='right', fontsize=12) # Add patches to color the X axis labels p1 = patches.Rectangle((.57, -0.005), width=.33, height=.13, alpha=.1, facecolor='green', transform=fig.transFigure) p2 = patches.Rectangle((.124, -0.005), width=.446, height=.13, alpha=.1, facecolor='red', transform=fig.transFigure) fig.add_artist(p1) fig.add_artist(p2) plt.show() 

 # Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean()) df.sort_values('cty', inplace=True) df.reset_index(inplace=True) # Draw plot fig, ax = plt.subplots(figsize=(16,10), dpi= 80) ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=2) ax.scatter(x=df.index, y=df.cty, s=75, color='firebrick', alpha=0.7) # Title, Label, Ticks and Ylim ax.set_title('Lollipop Chart for Highway Mileage', fontdict={'size':22}) ax.set_ylabel('Miles Per Gallon') ax.set_xticks(df.index) ax.set_xticklabels(df.manufacturer.str.upper(), rotation=60, fontdict={'horizontalalignment': 'right', 'size':12}) ax.set_ylim(0, 30) # Annotate for row in df.itertuples(): ax.text(row.Index, row.cty+.5, s=round(row.cty, 2), horizontalalignment= 'center', verticalalignment='bottom', fontsize=14) plt.show() 

 # Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean()) df.sort_values('cty', inplace=True) df.reset_index(inplace=True) # Draw plot fig, ax = plt.subplots(figsize=(16,10), dpi= 80) ax.hlines(y=df.index, xmin=11, xmax=26, color='gray', alpha=0.7, linewidth=1, linestyles='dashdot') ax.scatter(y=df.index, x=df.cty, s=75, color='firebrick', alpha=0.7) # Title, Label, Ticks and Ylim ax.set_title('Dot Plot for Highway Mileage', fontdict={'size':22}) ax.set_xlabel('Miles Per Gallon') ax.set_yticks(df.index) ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'}) ax.set_xlim(10, 27) plt.show() 

 import matplotlib.lines as mlines # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv") left_label = [str(c) + ', '+ str(round(y)) for c, y in zip(df.continent, df['1952'])] right_label = [str(c) + ', '+ str(round(y)) for c, y in zip(df.continent, df['1957'])] klass = ['red' if (y1-y2) < 0 else 'green' for y1, y2 in zip(df['1952'], df['1957'])] # draw line # https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941 def newline(p1, p2, color='black'): ax = plt.gca() l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='red' if p1[1]-p2[1] > 0 else 'green', marker='o', markersize=6) ax.add_line(l) return l fig, ax = plt.subplots(1,1,figsize=(14,14), dpi= 80) # Vertical Lines ax.vlines(x=1, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted') ax.vlines(x=3, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted') # Points ax.scatter(y=df['1952'], x=np.repeat(1, df.shape[0]), s=10, color='black', alpha=0.7) ax.scatter(y=df['1957'], x=np.repeat(3, df.shape[0]), s=10, color='black', alpha=0.7) # Line Segmentsand Annotation for p1, p2, c in zip(df['1952'], df['1957'], df['continent']): newline([1,p1], [3,p2]) ax.text(1-0.05, p1, c + ', ' + str(round(p1)), horizontalalignment='right', verticalalignment='center', fontdict={'size':14}) ax.text(3+0.05, p2, c + ', ' + str(round(p2)), horizontalalignment='left', verticalalignment='center', fontdict={'size':14}) # 'Before' and 'After' Annotations ax.text(1-0.05, 13000, 'BEFORE', horizontalalignment='right', verticalalignment='center', fontdict={'size':18, 'weight':700}) ax.text(3+0.05, 13000, 'AFTER', horizontalalignment='left', verticalalignment='center', fontdict={'size':18, 'weight':700}) # Decoration ax.set_title("Slopechart: Comparing GDP Per Capita between 1952 vs 1957", fontdict={'size':22}) ax.set(xlim=(0,4), ylim=(0,14000), ylabel='Mean GDP Per Capita') ax.set_xticks([1,3]) ax.set_xticklabels(["1952", "1957"]) plt.yticks(np.arange(500, 13000, 2000), fontsize=12) # Lighten borders plt.gca().spines["top"].set_alpha(.0) plt.gca().spines["bottom"].set_alpha(.0) plt.gca().spines["right"].set_alpha(.0) plt.gca().spines["left"].set_alpha(.0) plt.show() 

 import matplotlib.lines as mlines # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv") df.sort_values('pct_2014', inplace=True) df.reset_index(inplace=True) # Func to draw line segment def newline(p1, p2, color='black'): ax = plt.gca() l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='skyblue') ax.add_line(l) return l # Figure and Axes fig, ax = plt.subplots(1,1,figsize=(14,14), facecolor='#f7f7f7', dpi= 80) # Vertical Lines ax.vlines(x=.05, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted') ax.vlines(x=.10, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted') ax.vlines(x=.15, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted') ax.vlines(x=.20, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted') # Points ax.scatter(y=df['index'], x=df['pct_2013'], s=50, color='#0e668b', alpha=0.7) ax.scatter(y=df['index'], x=df['pct_2014'], s=50, color='#a3c4dc', alpha=0.7) # Line Segments for i, p1, p2 in zip(df['index'], df['pct_2013'], df['pct_2014']): newline([p1, i], [p2, i]) # Decoration ax.set_facecolor('#f7f7f7') ax.set_title("Dumbell Chart: Pct Change - 2013 vs 2014", fontdict={'size':22}) ax.set(xlim=(0,.25), ylim=(-1, 27), ylabel='Mean GDP Per Capita') ax.set_xticks([.05, .1, .15, .20]) ax.set_xticklabels(['5%', '15%', '20%', '25%']) ax.set_xticklabels(['5%', '15%', '20%', '25%']) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare data x_var = 'displ' groupby_var = 'class' df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var) vals = [df[x_var].values.tolist() for i, df in df_agg] # Draw plt.figure(figsize=(16,9), dpi= 80) colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))] n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22) plt.xlabel(x_var) plt.ylabel("Frequency") plt.ylim(0, 25) plt.xticks(ticks=bins[::3], labels=[round(b,1) for b in bins[::3]]) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare data x_var = 'manufacturer' groupby_var = 'class' df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var) vals = [df[x_var].values.tolist() for i, df in df_agg] # Draw plt.figure(figsize=(16,9), dpi= 80) colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))] n, bins, patches = plt.hist(vals, df[x_var].unique().__len__(), stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22) plt.xlabel(x_var) plt.ylabel("Frequency") plt.ylim(0, 40) plt.xticks(ticks=bins, labels=np.unique(df[x_var]).tolist(), rotation=90, horizontalalignment='left') plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(16,10), dpi= 80) sns.kdeplot(df.loc[df['cyl'] == 4, "cty"], shade=True, color="g", label="Cyl=4", alpha=.7) sns.kdeplot(df.loc[df['cyl'] == 5, "cty"], shade=True, color="deeppink", label="Cyl=5", alpha=.7) sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7) sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], shade=True, color="orange", label="Cyl=8", alpha=.7) # Decoration plt.title('Density Plot of City Mileage by n_Cylinders', fontsize=22) plt.legend() plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) sns.distplot(df.loc[df['class'] == 'compact', "cty"], color="dodgerblue", label="Compact", hist_kws={'alpha':.7}, kde_kws={'linewidth':3}) sns.distplot(df.loc[df['class'] == 'suv', "cty"], color="orange", label="SUV", hist_kws={'alpha':.7}, kde_kws={'linewidth':3}) sns.distplot(df.loc[df['class'] == 'minivan', "cty"], color="g", label="minivan", hist_kws={'alpha':.7}, kde_kws={'linewidth':3}) plt.ylim(0, 0.35) # Decoration plt.title('Density Plot of City Mileage by Vehicle Type', fontsize=22) plt.legend() plt.show() 

 # !pip install joypy # Import Data mpg = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(16,10), dpi= 80) fig, axes = joypy.joyplot(mpg, column=['hwy', 'cty'], by="class", ylim='own', figsize=(14,10)) # Decoration plt.title('Joy Plot of City and Highway Mileage by Class', fontsize=22) plt.show() 

 import matplotlib.patches as mpatches # Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") cyl_colors = {4:'tab:red', 5:'tab:green', 6:'tab:blue', 8:'tab:orange'} df_raw['cyl_color'] = df_raw.cyl.map(cyl_colors) # Mean and Median city mileage by make df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean()) df.sort_values('cty', ascending=False, inplace=True) df.reset_index(inplace=True) df_median = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.median()) # Draw horizontal lines fig, ax = plt.subplots(figsize=(16,10), dpi= 80) ax.hlines(y=df.index, xmin=0, xmax=40, color='gray', alpha=0.5, linewidth=.5, linestyles='dashdot') # Draw the Dots for i, make in enumerate(df.manufacturer): df_make = df_raw.loc[df_raw.manufacturer==make, :] ax.scatter(y=np.repeat(i, df_make.shape[0]), x='cty', data=df_make, s=75, edgecolors='gray', c='w', alpha=0.5) ax.scatter(y=i, x='cty', data=df_median.loc[df_median.index==make, :], s=75, c='firebrick') # Annotate ax.text(33, 13, "$red \; dots \; are \; the \: median$", fontdict={'size':12}, color='firebrick') # Decorations red_patch = plt.plot([],[], marker="o", ms=10, ls="", mec=None, color='firebrick', label="Median") plt.legend(handles=red_patch) ax.set_title('Distribution of City Mileage by Make', fontdict={'size':22}) ax.set_xlabel('Miles Per Gallon (City)', alpha=0.7) ax.set_yticks(df.index) ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'}, alpha=0.7) ax.set_xlim(1, 40) plt.xticks(alpha=0.7) plt.gca().spines["top"].set_visible(False) plt.gca().spines["bottom"].set_visible(False) plt.gca().spines["right"].set_visible(False) plt.gca().spines["left"].set_visible(False) plt.grid(axis='both', alpha=.4, linewidth=.1) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) sns.boxplot(x='class', y='hwy', data=df, notch=False) # Add N Obs inside boxplot (optional) def add_n_obs(df,group_col,y): medians_dict = {grp[0]:grp[1][y].median() for grp in df.groupby(group_col)} xticklabels = [x.get_text() for x in plt.gca().get_xticklabels()] n_obs = df.groupby(group_col)[y].size().values for (x, xticklabel), n_ob in zip(enumerate(xticklabels), n_obs): plt.text(x, medians_dict[xticklabel]*1.01, "#obs : "+str(n_ob), horizontalalignment='center', fontdict={'size':14}, color='white') add_n_obs(df,group_col='class',y='hwy') # Decoration plt.title('Box Plot of Highway Mileage by Vehicle Class', fontsize=22) plt.ylim(10, 40) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) sns.boxplot(x='class', y='hwy', data=df, hue='cyl') sns.stripplot(x='class', y='hwy', data=df, color='black', size=3, jitter=1) for i in range(len(df['class'].unique())-1): plt.vlines(i+.5, 10, 45, linestyles='solid', colors='gray', alpha=0.2) # Decoration plt.title('Box Plot of Highway Mileage by Vehicle Class', fontsize=22) plt.legend(title='Cylinders') plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) sns.violinplot(x='class', y='hwy', data=df, scale='width', inner='quartile') # Decoration plt.title('Violin Plot of Highway Mileage by Vehicle Class', fontsize=22) plt.show() 

 # Read data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) group_col = 'Gender' order_of_bars = df.Stage.unique()[::-1] colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in range(len(df[group_col].unique()))] for c, group in zip(colors, df[group_col].unique()): sns.barplot(x='Users', y='Stage', data=df.loc[df[group_col]==group, :], order=order_of_bars, color=c, label=group) # Decorations plt.xlabel("$Users$") plt.ylabel("Stage of Purchase") plt.yticks(fontsize=12) plt.title("Population Pyramid of the Marketing Funnel", fontsize=22) plt.legend() plt.show() 

 # Load Dataset titanic = sns.load_dataset("titanic") # Plot g = sns.catplot("alive", col="deck", col_wrap=4, data=titanic[titanic.deck.notnull()], kind="count", height=3.5, aspect=.8, palette='tab20') fig.suptitle('sf') plt.show() 

 # Load Dataset titanic = sns.load_dataset("titanic") # Plot sns.catplot(x="age", y="embark_town", hue="sex", col="class", data=titanic[titanic.embark_town.notnull()], orient="h", height=5, aspect=1, palette="tab10", kind="violin", dodge=True, cut=0, bw=.2) 

 #! pip install pywaffle # Reference: https://stackoverflow.com/questions/41400136/how-to-do-waffle-charts-in-python-square-piechart from pywaffle import Waffle # Import df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size().reset_index(name='counts') n_categories = df.shape[0] colors = [plt.cm.inferno_r(i/float(n_categories)) for i in range(n_categories)] # Draw Plot and Decorate fig = plt.figure( FigureClass=Waffle, plots={ '111': { 'values': df['counts'], 'labels': ["{0} ({1})".format(n[0], n[1]) for n in df[['class', 'counts']].itertuples()], 'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12}, 'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18} }, }, rows=7, colors=colors, figsize=(16, 9) ) 

 #! pip install pywaffle from pywaffle import Waffle # Import # df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data # By Class Data df_class = df_raw.groupby('class').size().reset_index(name='counts_class') n_categories = df_class.shape[0] colors_class = [plt.cm.Set3(i/float(n_categories)) for i in range(n_categories)] # By Cylinders Data df_cyl = df_raw.groupby('cyl').size().reset_index(name='counts_cyl') n_categories = df_cyl.shape[0] colors_cyl = [plt.cm.Spectral(i/float(n_categories)) for i in range(n_categories)] # By Make Data df_make = df_raw.groupby('manufacturer').size().reset_index(name='counts_make') n_categories = df_make.shape[0] colors_make = [plt.cm.tab20b(i/float(n_categories)) for i in range(n_categories)] # Draw Plot and Decorate fig = plt.figure( FigureClass=Waffle, plots={ '311': { 'values': df_class['counts_class'], 'labels': ["{1}".format(n[0], n[1]) for n in df_class[['class', 'counts_class']].itertuples()], 'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Class'}, 'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18}, 'colors': colors_class }, '312': { 'values': df_cyl['counts_cyl'], 'labels': ["{1}".format(n[0], n[1]) for n in df_cyl[['cyl', 'counts_cyl']].itertuples()], 'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Cyl'}, 'title': {'label': '# Vehicles by Cyl', 'loc': 'center', 'fontsize':18}, 'colors': colors_cyl }, '313': { 'values': df_make['counts_make'], 'labels': ["{1}".format(n[0], n[1]) for n in df_make[['manufacturer', 'counts_make']].itertuples()], 'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Manufacturer'}, 'title': {'label': '# Vehicles by Make', 'loc': 'center', 'fontsize':18}, 'colors': colors_make } }, rows=9, figsize=(16, 14) ) 

 # Import df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size() # Make the plot with pandas df.plot(kind='pie', subplots=True, figsize=(8, 8), dpi= 80) plt.title("Pie Chart of Vehicle Class - Bad") plt.ylabel("") plt.show() 

 # Import df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size().reset_index(name='counts') # Draw Plot fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80) data = df['counts'] categories = df['class'] explode = [0,0,0,0,0,0.1,0] def func(pct, allvals): absolute = int(pct/100.*np.sum(allvals)) return "{:.1f}% ({:d} )".format(pct, absolute) wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data), textprops=dict(color="w"), colors=plt.cm.Dark2.colors, startangle=140, explode=explode) # Decoration ax.legend(wedges, categories, title="Vehicle Class", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1)) plt.setp(autotexts, size=10, weight=700) ax.set_title("Class of Vehicles: Pie Chart") plt.show() 

 # pip install squarify import squarify # Import Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size().reset_index(name='counts') labels = df.apply(lambda x: str(x[0]) + "\n (" + str(x[1]) + ")", axis=1) sizes = df['counts'].values.tolist() colors = [plt.cm.Spectral(i/float(len(labels))) for i in range(len(labels))] # Draw Plot plt.figure(figsize=(12,8), dpi= 80) squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8) # Decorate plt.title('Treemap of Vechile Class') plt.axis('off') plt.show() 

 import random # Import Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('manufacturer').size().reset_index(name='counts') n = df['manufacturer'].unique().__len__()+1 all_colors = list(plt.cm.colors.cnames.keys()) random.seed(100) c = random.choices(all_colors, k=n) # Plot Bars plt.figure(figsize=(16,10), dpi= 80) plt.bar(df['manufacturer'], df['counts'], color=c, width=.5) for i, val in enumerate(df['counts'].values): plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12}) # Decoration plt.gca().set_xticklabels(df['manufacturer'], rotation=60, horizontalalignment= 'right') plt.title("Number of Vehicles by Manaufacturers", fontsize=22) plt.ylabel('# Vehicles') plt.ylim(0, 45) plt.show() 

 # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') # Draw Plot plt.figure(figsize=(16,10), dpi= 80) plt.plot('date', 'traffic', data=df, color='tab:red') # Decoration plt.ylim(50, 750) xtick_location = df.index.tolist()[::12] xtick_labels = [x[-4:] for x in df.date.tolist()[::12]] plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7) plt.yticks(fontsize=12, alpha=.7) plt.title("Air Passengers Traffic (1949 - 1969)", fontsize=22) plt.grid(axis='both', alpha=.3) # Remove borders plt.gca().spines["top"].set_alpha(0.0) plt.gca().spines["bottom"].set_alpha(0.3) plt.gca().spines["right"].set_alpha(0.0) plt.gca().spines["left"].set_alpha(0.3) plt.show() 

 # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') # Get the Peaks and Troughs data = df['traffic'].values doublediff = np.diff(np.sign(np.diff(data))) peak_locations = np.where(doublediff == -2)[0] + 1 doublediff2 = np.diff(np.sign(np.diff(-1*data))) trough_locations = np.where(doublediff2 == -2)[0] + 1 # Draw Plot plt.figure(figsize=(16,10), dpi= 80) plt.plot('date', 'traffic', data=df, color='tab:blue', label='Air Traffic') plt.scatter(df.date[peak_locations], df.traffic[peak_locations], marker=mpl.markers.CARETUPBASE, color='tab:green', s=100, label='Peaks') plt.scatter(df.date[trough_locations], df.traffic[trough_locations], marker=mpl.markers.CARETDOWNBASE, color='tab:red', s=100, label='Troughs') # Annotate for t, p in zip(trough_locations[1::5], peak_locations[::3]): plt.text(df.date[p], df.traffic[p]+15, df.date[p], horizontalalignment='center', color='darkgreen') plt.text(df.date[t], df.traffic[t]-35, df.date[t], horizontalalignment='center', color='darkred') # Decoration plt.ylim(50,750) xtick_location = df.index.tolist()[::6] xtick_labels = df.date.tolist()[::6] plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=90, fontsize=12, alpha=.7) plt.title("Peak and Troughs of Air Passengers Traffic (1949 - 1969)", fontsize=22) plt.yticks(fontsize=12, alpha=.7) # Lighten borders plt.gca().spines["top"].set_alpha(.0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(.0) plt.gca().spines["left"].set_alpha(.3) plt.legend(loc='upper left') plt.grid(axis='y', alpha=.3) plt.show() 

 from statsmodels.graphics.tsaplots import plot_acf, plot_pacf # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') # Draw Plot fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80) plot_acf(df.traffic.tolist(), ax=ax1, lags=50) plot_pacf(df.traffic.tolist(), ax=ax2, lags=20) # Decorate # lighten the borders ax1.spines["top"].set_alpha(.3); ax2.spines["top"].set_alpha(.3) ax1.spines["bottom"].set_alpha(.3); ax2.spines["bottom"].set_alpha(.3) ax1.spines["right"].set_alpha(.3); ax2.spines["right"].set_alpha(.3) ax1.spines["left"].set_alpha(.3); ax2.spines["left"].set_alpha(.3) # font size of tick labels ax1.tick_params(axis='both', labelsize=12) ax2.tick_params(axis='both', labelsize=12) plt.show() 

 import statsmodels.tsa.stattools as stattools # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/mortality.csv') x = df['mdeaths'] y = df['fdeaths'] # Compute Cross Correlations ccs = stattools.ccf(x, y)[:100] nlags = len(ccs) # Compute the Significance level # ref: https://stats.stackexchange.com/questions/3115/cross-correlation-significance-in-r/3128#3128 conf_level = 2 / np.sqrt(nlags) # Draw Plot plt.figure(figsize=(12,7), dpi= 80) plt.hlines(0, xmin=0, xmax=100, color='gray') # 0 axis plt.hlines(conf_level, xmin=0, xmax=100, color='gray') plt.hlines(-conf_level, xmin=0, xmax=100, color='gray') plt.bar(x=np.arange(len(ccs)), height=ccs, width=.3) # Decoration plt.title('$Cross\; Correlation\; Plot:\; mdeaths\; vs\; fdeaths$', fontsize=22) plt.xlim(0,len(ccs)) plt.show() 

 from statsmodels.tsa.seasonal import seasonal_decompose from dateutil.parser import parse # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') dates = pd.DatetimeIndex([parse(d).strftime('%Y-%m-01') for d in df['date']]) df.set_index(dates, inplace=True) # Decompose result = seasonal_decompose(df['traffic'], model='multiplicative') # Plot plt.rcParams.update({'figure.figsize': (10,10)}) result.plot().suptitle('Time Series Decomposition of Air Passengers') plt.show() 

 # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/mortality.csv') # Define the upper limit, lower limit, interval of Y axis and colors y_LL = 100 y_UL = int(df.iloc[:, 1:].max().max()*1.1) y_interval = 400 mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange'] # Draw Plot and Annotate fig, ax = plt.subplots(1,1,figsize=(16, 9), dpi= 80) columns = df.columns[1:] for i, column in enumerate(columns): plt.plot(df.date.values, df .values, lw=1.5, color=mycolors[i]) plt.text(df.shape[0]+1, df .values[-1], column, fontsize=14, color=mycolors[i]) # Draw Tick lines for y in range(y_LL, y_UL, y_interval): plt.hlines(y, xmin=0, xmax=71, colors='black', alpha=0.3, linestyles="--", lw=0.5) # Decorations plt.tick_params(axis="both", which="both", bottom=False, top=False, labelbottom=True, left=False, right=False, labelleft=True) # Lighten borders plt.gca().spines["top"].set_alpha(.3) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(.3) plt.gca().spines["left"].set_alpha(.3) plt.title('Number of Deaths from Lung Diseases in the UK (1974-1979)', fontsize=22) plt.yticks(range(y_LL, y_UL, y_interval), [str(y) for y in range(y_LL, y_UL, y_interval)], fontsize=12) plt.xticks(range(0, df.shape[0], 12), df.date.values[::12], horizontalalignment='left', fontsize=12) plt.ylim(y_LL, y_UL) plt.xlim(-2, 80) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv") x = df['date'] y1 = df['psavert'] y2 = df['unemploy'] # Plot Line1 (Left Y Axis) fig, ax1 = plt.subplots(1,1,figsize=(16,9), dpi= 80) ax1.plot(x, y1, color='tab:red') # Plot Line2 (Right Y Axis) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis ax2.plot(x, y2, color='tab:blue') # Decorations # ax1 (left Y axis) ax1.set_xlabel('Year', fontsize=20) ax1.tick_params(axis='x', rotation=0, labelsize=12) ax1.set_ylabel('Personal Savings Rate', color='tab:red', fontsize=20) ax1.tick_params(axis='y', rotation=0, labelcolor='tab:red' ) ax1.grid(alpha=.4) # ax2 (right Y axis) ax2.set_ylabel("# Unemployed (1000's)", color='tab:blue', fontsize=20) ax2.tick_params(axis='y', labelcolor='tab:blue') ax2.set_xticks(np.arange(0, len(x), 60)) ax2.set_xticklabels(x[::60], rotation=90, fontdict={'fontsize':10}) ax2.set_title("Personal Savings Rate vs Unemployed: Plotting in Secondary Y Axis", fontsize=22) fig.tight_layout() plt.show() 

 from scipy.stats import sem # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/user_orders_hourofday.csv") df_mean = df.groupby('order_hour_of_day').quantity.mean() df_se = df.groupby('order_hour_of_day').quantity.apply(sem).mul(1.96) # Plot plt.figure(figsize=(16,10), dpi= 80) plt.ylabel("# Orders", fontsize=16) x = df_mean.index plt.plot(x, df_mean, color="white", lw=2) plt.fill_between(x, df_mean - df_se, df_mean + df_se, color="#3F5D7D") # Decorations # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(1) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(1) plt.xticks(x[::2], [str(d) for d in x[::2]] , fontsize=12) plt.title("User Orders by Hour of Day (95% confidence)", fontsize=22) plt.xlabel("Hour of Day") s, e = plt.gca().get_xlim() plt.xlim(s, e) # Draw Horizontal Tick lines for y in range(8, 20, 2): plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5) plt.show() 

 "Data Source: https://www.kaggle.com/olistbr/brazilian-ecommerce#olist_orders_dataset.csv" from dateutil.parser import parse from scipy.stats import sem # Import Data df_raw = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/orders_45d.csv', parse_dates=['purchase_time', 'purchase_date']) # Prepare Data: Daily Mean and SE Bands df_mean = df_raw.groupby('purchase_date').quantity.mean() df_se = df_raw.groupby('purchase_date').quantity.apply(sem).mul(1.96) # Plot plt.figure(figsize=(16,10), dpi= 80) plt.ylabel("# Daily Orders", fontsize=16) x = [d.date().strftime('%Y-%m-%d') for d in df_mean.index] plt.plot(x, df_mean, color="white", lw=2) plt.fill_between(x, df_mean - df_se, df_mean + df_se, color="#3F5D7D") # Decorations # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(1) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(1) plt.xticks(x[::6], [str(d) for d in x[::6]] , fontsize=12) plt.title("Daily Order Quantity of Brazilian Retail with Error Bands (95% confidence)", fontsize=20) # Axis limits s, e = plt.gca().get_xlim() plt.xlim(s, e-2) plt.ylim(4, 10) # Draw Horizontal Tick lines for y in range(5, 10, 1): plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5) plt.show() 

 # Import Data df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/nightvisitors.csv') # Decide Colors mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive'] # Draw Plot and Annotate fig, ax = plt.subplots(1,1,figsize=(16, 9), dpi= 80) columns = df.columns[1:] labs = columns.values.tolist() # Prepare data x = df['yearmon'].values.tolist() y0 = df[columns[0]].values.tolist() y1 = df[columns[1]].values.tolist() y2 = df[columns[2]].values.tolist() y3 = df[columns[3]].values.tolist() y4 = df[columns[4]].values.tolist() y5 = df[columns[5]].values.tolist() y6 = df[columns[6]].values.tolist() y7 = df[columns[7]].values.tolist() y = np.vstack([y0, y2, y4, y6, y7, y5, y1, y3]) # Plot for each column labs = columns.values.tolist() ax = plt.gca() ax.stackplot(x, y, labels=labs, colors=mycolors, alpha=0.8) # Decorations ax.set_title('Night Visitors in Australian Regions', fontsize=18) ax.set(ylim=[0, 100000]) ax.legend(fontsize=10, ncol=4) plt.xticks(x[::5], fontsize=10, horizontalalignment='center') plt.yticks(np.arange(10000, 100000, 20000), fontsize=10) plt.xlim(x[0], x[-1]) # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(.3) plt.show() 

 # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv") # Prepare Data x = df['date'].values.tolist() y1 = df['psavert'].values.tolist() y2 = df['uempmed'].values.tolist() mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive'] columns = ['psavert', 'uempmed'] # Draw Plot fig, ax = plt.subplots(1, 1, figsize=(16,9), dpi= 80) ax.fill_between(x, y1=y1, y2=0, label=columns[1], alpha=0.5, color=mycolors[1], linewidth=2) ax.fill_between(x, y1=y2, y2=0, label=columns[0], alpha=0.5, color=mycolors[0], linewidth=2) # Decorations ax.set_title('Personal Savings Rate vs Median Duration of Unemployment', fontsize=18) ax.set(ylim=[0, 30]) ax.legend(loc='best', fontsize=12) plt.xticks(x[::50], fontsize=10, horizontalalignment='center') plt.yticks(np.arange(2.5, 30.0, 2.5), fontsize=10) plt.xlim(-10, x[-1]) # Draw Tick lines for y in np.arange(2.5, 30.0, 2.5): plt.hlines(y, xmin=0, xmax=len(x), colors='black', alpha=0.3, linestyles="--", lw=0.5) # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(.3) plt.show() 

 import matplotlib as mpl import calmap # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv", parse_dates=['date']) df.set_index('date', inplace=True) # Plot plt.figure(figsize=(16,10), dpi= 80) calmap.calendarplot(df['2014']['VIX.Close'], fig_kws={'figsize': (16,10)}, yearlabel_kws={'color':'black', 'fontsize':14}, subplot_kws={'title':'Yahoo Stock Prices'}) plt.show() 

 from dateutil.parser import parse # Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') # Prepare data df['year'] = [parse(d).year for d in df.date] df['month'] = [parse(d).strftime('%b') for d in df.date] years = df['year'].unique() # Draw Plot mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive', 'deeppink', 'steelblue', 'firebrick', 'mediumseagreen'] plt.figure(figsize=(16,10), dpi= 80) for i, y in enumerate(years): plt.plot('month', 'traffic', data=df.loc[df.year==y, :], color=mycolors[i], label=y) plt.text(df.loc[df.year==y, :].shape[0]-.9, df.loc[df.year==y, 'traffic'][-1:].values[0], y, fontsize=12, color=mycolors[i]) # Decoration plt.ylim(50,750) plt.xlim(-0.3, 11) plt.ylabel('$Air Traffic$') plt.yticks(fontsize=12, alpha=.7) plt.title("Monthly Seasonal Plot: Air Passengers Traffic (1949 - 1969)", fontsize=22) plt.grid(axis='y', alpha=.3) # Remove borders plt.gca().spines["top"].set_alpha(0.0) plt.gca().spines["bottom"].set_alpha(0.5) plt.gca().spines["right"].set_alpha(0.0) plt.gca().spines["left"].set_alpha(0.5) # plt.legend(loc='upper right', ncol=2, fontsize=12) plt.show() 

 import scipy.cluster.hierarchy as shc # Import Data df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv') # Plot plt.figure(figsize=(16, 10), dpi= 80) plt.title("USArrests Dendograms", fontsize=22) dend = shc.dendrogram(shc.linkage(df[['Murder', 'Assault', 'UrbanPop', 'Rape']], method='ward'), labels=df.State.values, color_threshold=100) plt.xticks(fontsize=12) plt.show() 

 from sklearn.cluster import AgglomerativeClustering from scipy.spatial import ConvexHull # Import Data df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv') # Agglomerative Clustering cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward') cluster.fit_predict(df[['Murder', 'Assault', 'UrbanPop', 'Rape']]) # Plot plt.figure(figsize=(14, 10), dpi= 80) plt.scatter(df.iloc[:,0], df.iloc[:,1], c=cluster.labels_, cmap='tab10') # Encircle def encircle(x,y, ax=None, **kw): if not ax: ax=plt.gca() p = np.c_[x,y] hull = ConvexHull(p) poly = plt.Polygon(p[hull.vertices,:], **kw) ax.add_patch(poly) # Draw polygon surrounding vertices encircle(df.loc[cluster.labels_ == 0, 'Murder'], df.loc[cluster.labels_ == 0, 'Assault'], ec="k", fc="gold", alpha=0.2, linewidth=0) encircle(df.loc[cluster.labels_ == 1, 'Murder'], df.loc[cluster.labels_ == 1, 'Assault'], ec="k", fc="tab:blue", alpha=0.2, linewidth=0) encircle(df.loc[cluster.labels_ == 2, 'Murder'], df.loc[cluster.labels_ == 2, 'Assault'], ec="k", fc="tab:red", alpha=0.2, linewidth=0) encircle(df.loc[cluster.labels_ == 3, 'Murder'], df.loc[cluster.labels_ == 3, 'Assault'], ec="k", fc="tab:green", alpha=0.2, linewidth=0) encircle(df.loc[cluster.labels_ == 4, 'Murder'], df.loc[cluster.labels_ == 4, 'Assault'], ec="k", fc="tab:orange", alpha=0.2, linewidth=0) # Decorations plt.xlabel('Murder'); plt.xticks(fontsize=12) plt.ylabel('Assault'); plt.yticks(fontsize=12) plt.title('Agglomerative Clustering of USArrests (5 Groups)', fontsize=22) plt.show() 

 from pandas.plotting import andrews_curves # Import df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv") df.drop(['cars', 'carname'], axis=1, inplace=True) # Plot plt.figure(figsize=(12,9), dpi= 80) andrews_curves(df, 'cyl', colormap='Set1') # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(.3) plt.title('Andrews Curves of mtcars', fontsize=22) plt.xlim(-3,3) plt.grid(alpha=0.3) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.show() 

 from pandas.plotting import parallel_coordinates # Import Data df_final = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/diamonds_filter.csv") # Plot plt.figure(figsize=(12,9), dpi= 80) parallel_coordinates(df_final, 'cut', colormap='Dark2') # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(.3) plt.title('Parallel Coordinated of Diamonds', fontsize=22) plt.grid(alpha=0.3) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.show()