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感謝關注趣學程序！公衆號內部回覆666獲取熱門教程
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來源丨Python大本營
大家好，我是小F～
今天分享給大家25個Matplotlib圖的彙總，在數據分析和可視化中最有用。
# !pip install brewer2mpl
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
# 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)
# 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.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()
# 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
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
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
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()
# 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()
# 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()
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