重磅干货,第一时间送达 import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import warnings; warnings.filterwarnings(action='once') large = 22; med = 16; small = 12 params = {'axes.titlesize': large, 'legend.fontsize': med, 'figure.figsize': (16, 10), 'axes.labelsize': med, 'axes.titlesize': med, 'xtick.labelsize': med, 'ytick.labelsize': med, 'figure.titlesize': large} plt.rcParams.update(params) plt.style.use('seaborn-whitegrid') sns.set_style('white') %matplotlib inline # Version print(mpl.__version__) #> 3.0.0 print(sns.__version__) #> 0.9.0 midwest = pd.read_csv('https://raw./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 scipy.spatial import ConvexHull import warnings; warnings.simplefilter('ignore') sns.set_style('white') # Step 1: Prepare Data midwest = pd.read_csv('https://raw./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:///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() df = pd.read_csv('https://raw./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) df = pd.read_csv('https://raw./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() df = pd.read_csv('https://raw./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() df = pd.read_csv('https://raw./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.stripplot(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() df = pd.read_csv('https://raw./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 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() df = pd.read_csv('https://raw./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 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() 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() 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() df = sns.load_dataset('iris') # Plot plt.figure(figsize=(10,8), dpi= 80) sns.pairplot(df, kind='reg', hue='species') plt.show() 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() 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() 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() 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 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 1975', 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() 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() 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() 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 Data df = pd.read_csv('https://raw./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:///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 Data df = pd.read_csv('https://raw./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() 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() 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() 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() 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() # 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() # 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() |
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