A collection of 50 Python thesis drawings, from entry to proficiency with code
Share a good python drawing resource today:
https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
image-20230324232925848
This website provides the most common 50 Python drawing (Matplotlib/Seaborn) methods and codes for scientific research papers.
Grouped according to 7 different uses of visualization goals. For example, if you want to plot the relationship between two variables, look at the graphs under the Correlation section. Or, if you want to show how a value changes over time, check out the Change section, etc.
This article shows some excellent cases, the complete code can be found on the website, and the data can be found below:
Correlation
Correlograms are used to visualize the relationship between 2 or more variables. That is, how one variable changes relative to another.
Linear regression fit
The line of best fit is a great way to see how two variables vary with each other. The figure below shows how the line of best fit differs between groups in the data. To disable grouping and only draw a line of best fit for the entire dataset, remove the following parameters. hue='cyl'``sns.lmplot()
import seaborn as sns
# Import Data
df = pd.read_csv("./python/matplotlib-data/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()
marginal boxplot
Marginal boxplots serve a similar purpose to marginal histograms. Boxplots help determine the median, 25th, and 75th percentiles of X and Y.
import pandas as pd
# Import Data
df = pd.read_csv("./python/matplotlib-data/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()
correlation diagram
Correlograms are used to visually view correlation measures between all possible pairs of numeric variables in a given two-dimensional array.
# Import Dataset
df = pd.read_csv("./python/matplotlib-data/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()
Deviation
Lollipop diagram with markers
# Prepare Data
df = pd.read_csv("./python/matplotlib-data/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()
area chart
The area chart is a relatively new drawing, it is recommended to master
By coloring the area between the axis and the line, the area chart puts more emphasis not only on the peaks and troughs, but also on the duration of the highs and lows. The longer the high lasts, the larger the area below the line.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Prepare Data
df = pd.read_csv("./python/matplotlib-data/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()
Ranking
Ranking indicates that the data has obvious ranks, and ordered bar charts, lollipop charts and dumbbell charts are recommended
ordered bar chart
An ordered bar chart effectively communicates the ranking order of items. It is a classic method of visualization. The highlight is to add the value of the indicator above the chart, and the user can get accurate information from the chart itself. And the text below has been color-graded and can be learned.
# Prepare Data
df_raw = pd.read_csv("./python/matplotlib-data/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()
dumbbell illustration
The dumbbell diagram conveys the maximum and minimum positions of various items and the ranking order of the items.
import matplotlib.lines as mlines
# Import Data
df = pd.read_csv("./python/matplotlib-data/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()
Distribution
Introducing multivariate histograms, density plots, distribution point plots (newer plotting methods)
Skip the traditional box plot, violin plot
multivariate histogram
A histogram for a categorical variable shows the frequency distribution of that variable. Visualize the distribution in relation to another categorical variable representing color by coloring the bars.
# Import Data
df = pd.read_csv("./python/matplotlib-data/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()
density map
Density plots are a commonly used tool for visualizing the distribution of continuous variables. By grouping them by class variables, you can explore relationships and differences between variables.
A density curve with a histogram brings together the collective information conveyed by both plots, allowing them to be both placed in one plot.
# Import Data
import seaborn as sns
df = pd.read_csv("./python/matplotlib-data/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()
Distributed point map
Distribution point plots display the univariate distribution of points split by group. The darker the point, the more concentrated the data points in that area. By coloring the medians differently, the true positioning of the groups becomes immediately apparent.
import matplotlib.patches as mpatches
# Prepare Data
df_raw = pd.read_csv("./python/matplotlib-data/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=np.repeat(i, df_make.shape[0]), x=df_make['cty'], 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()
Composition
Introduction to Waffle Diagrams, Tree Diagrams
Omit the traditional pie chart
Waffle Figure
A newer plot showing the distribution of variables
#! 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("./python/matplotlib-data/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)
)
tree diagram
The tree diagram is similar to the pie chart, the advantage is that it does not mislead the contribution of each group, which is better than the pie chart.
# pip install squarify
import squarify
# Import Data
df_raw = pd.read_csv("./python/matplotlib-data/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()
Change
Mainly display time series data, introduce peak and trough time series and double y-axis time series
peak and trough time series
Time series plots are used to visualize how a given metric changes over time. The chart below shows how air passenger traffic has changed between 1949 and 1969.
The time series below plots all the peaks and troughs and annotates the occurrence of selected special events.
# Import Data
df = pd.read_csv('./python/matplotlib-data/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()
Dual y-axis time series
If you want to show two time series measuring two different quantities at the same point in time, you can plot the second series on the second Y-axis on the right.
# Import Data
df = pd.read_csv("./python/matplotlib-data/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()
Groups
Introduction to dendrograms and cluster plots
Tree
import scipy.cluster.hierarchy as shc
# Import Data
df = pd.read_csv('./python/matplotlib-data/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()
Cluster diagram
Cluster plots can be used to divide points belonging to the same cluster, and can use the first to principal components as the X-axis and Y-axis.
from sklearn.cluster import AgglomerativeClustering
from scipy.spatial import ConvexHull
# Import Data
df = pd.read_csv('./python/matplotlib-data/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()
Summarize
This article only shows part of the TOP 50 common drawings, please check the complete code directly on the website:
https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
Please reply all the data in the background [python drawing]
image-20230325234912188
This article is written by mdnice Multi-platform publishing