50个常用统计图表代码总结(5）
21. Histogram for Categorical Variable
The histogram of a categorical variable shows the frequency distribution of a that variable. By coloring the bars, you can visualize the distribution in connection with another categorical variable representing the colors.
# 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[:,].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, df.unique().__len__(), 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(0,40)
plt.xticks(ticks=bins, labels=np.unique(df).tolist(), rotation=90, horizontalalignment='left')
plt.show()
22. Density Plot
Density plots are a commonly used tool visualise the distribution of a continuous variable. By grouping them by the ‘response’ variable, you can inspect the relationship between the X and the Y. The below case if for representational purpose to describe how the distribution of city mileage varies with respect the number of cylinders.
# 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==4,"cty"], shade=True, color="g", label="Cyl=4", alpha=.7)
sns.kdeplot(df.loc==5,"cty"], shade=True, color="deeppink", label="Cyl=5", alpha=.7)
sns.kdeplot(df.loc==6,"cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7)
sns.kdeplot(df.loc==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()
plt.show()
23. Density Curves with Histogram
Density curve with histogram brings together the collective information conveyed by the two plots so you can have them both in a single figure instead of two.
# 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=='compact',"cty"], color="dodgerblue", label="Compact", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})
sns.distplot(df.loc=='suv',"cty"], color="orange", label="SUV", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})
sns.distplot(df.loc=='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()
24. Joy Plot
Joy Plot allows the density curves of different groups to overlap, it is a great way to visualize the distribution of a larger number of groups in relation to each other. It looks pleasing to the eye and conveys just the right information clearly. It can be easily built using the joypy package which is based on matplotlib.
# !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()
25. Distributed Dot Plot
Distributed dot plot shows the univariate distribution of points segmented by groups. The darker the points, more is the concentration of data points in that region. By coloring the median differently, the real positioning of the groups becomes apparent instantly.
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
ax.scatter(y=np.repeat(i, df_make.shape), x='cty', data=df_make, s=75, edgecolors='gray', c='w', alpha=0.5)
ax.scatter(y=i, x='cty', data=df_median.loc, 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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