50个常用统计图表代码总结(4）
16. Lollipop Chart
Lollipop chart serves a similar purpose as a ordered bar chart in a visually pleasing way.
# 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()
17. Dot Plot
The dot plot conveys the rank order of the items. And since it is aligned along the horizontal axis, you can visualize how far the points are from each other more easily.
# 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()
18. Slope Chart
Slope chart is most suitable for comparing the ‘Before’ and ‘After’ positions of a given person/item.
import matplotlib.lines as mlines
# Import Data
df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
left_label =)]
right_label =)]
klass =['red'if(y1-y2)<0else'green'for y1, y2 in zip(df['1952'], df['1957'])]
# draw line
# <a href="https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941" target="_blank">https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941</a>
def newline(p1, p2, color='black'):
ax = plt.gca()
l = mlines.Line2D(,p2],,p2], color='red'if p1-p2>0else'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), s=10, color='black', alpha=0.7)
ax.scatter(y=df['1957'], x=np.repeat(3, df.shape), s=10, color='black', alpha=0.7)
# Line Segmentsand Annotation
for p1, p2, c in zip(df['1952'], df['1957'], df['continent']):
newline(,)
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()
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()
19. Dumbbell Plot
Dumbbell plot conveys the ‘before’ and ‘after’ positions of various items along with the rank ordering of the items. Its very useful if you want to visualize the effect of a particular project / initiative on different objects.
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(,p2],,p2], 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(,)
# 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
20. Histogram for Continuous Variable
Histogram shows the frequency distribution of a given variable. The below representation groups the frequency bars based on a categorical variable giving a greater insight about the continuous variable and the categorical variable in tandem. Create histogram and learn how to interpret them in this free video tutorial.
# 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[:,].groupby(groupby_var)
vals =.values.tolist()for i, df in df_agg]
# Draw
plt.figure(figsize=(16,9), dpi=80)
colors =
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).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=])
plt.show()
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