50个常用统计图表代码总结(3）
11. Diverging Texts
Diverging texts is similar to diverging bars and it preferred if you want to show the value of each items within the chart in a nice and presentable way.
# 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 <0else'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 <0else'left',
verticalalignment='center', fontdict={'color':'red'if x <0else'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()
12. Diverging Dot Plot
Divering dot plot is also similar to the diverging bars. However compared to diverging bars, the absence of bars reduces the amount of contrast and disparity between the groups.
# 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 <0else'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()
13. Diverging Lollipop Chart with Markers
Lollipop with markers provides a flexible way of visualizing the divergence by laying emphasis on any significant datapoints you want to bring attention to and give reasoning within the chart appropriately.
# 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='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=, 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()
14. Area Chart
By coloring the area between the axis and the lines, the area chart throws more emphasis not just on the peaks and troughs but also the duration of the highs and lows. The longer the duration of the highs, the larger is the area under the line.
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)
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, y_returns,0, where=y_returns>=0, facecolor='green', interpolate=True, alpha=0.7)
plt.fill_between(x, y_returns,0, where=y_returns<=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 =.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
15. Ordered Bar Chart
Ordered bar chart conveys the rank order of the items effectively. But adding the value of the metric above the chart, the user gets the precise information from the chart itself. It is a classic way of visualizing items based on counts or any given metric. Check this free video tutorial onimplementing and interpreting ordered bar charts.
# 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()
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