python绘制发散型柱状图+误差阴影时间序列图+双坐标系时间序列图+绘制金字塔图

1.绘制发散型柱状图

2.绘制带误差阴影的时间序列图

3.绘制双坐标系时间序列图

4.绘制金字塔图

1.绘制发散型柱状图

python绘制发散型柱状图，展示单个指标的变化的顺序和数量，在柱子上添加了数值文本。

实现代码：

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')
df = pd.read_csv("C:\工作\学习\数据杂坛/datasets/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=(10, 6), dpi=80)
plt.hlines(y=df.index,
           xmin=0,
           xmax=df.mpg_z,
           color=df.colors,
           alpha=0.8,
           linewidth=5)
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':'black' if x < 0 else 'black', 'size':10})
# Decorations
plt.gca().set(ylabel='$Model', xlabel='$Mileage')
plt.yticks(df.index, df.cars, fontsize=12)
plt.xticks(fontsize=12)
plt.title('Diverging Bars of Car Mileage')
plt.grid(linestyle='--', alpha=0.5)
plt.show()

实现效果：

2.绘制带误差阴影的时间序列图

实现功能：

python绘制带误差阴影的时间序列图。

实现代码：

from scipy.stats import sem
import pandas as pd
import matplotlib.pyplot as plt
# Import Data
df_raw = pd.read_csv('F:\数据杂坛\datasets\orders_45d.csv',
                     parse_dates=['purchase_time', 'purchase_date'])
# Prepare Data: Daily Mean and SE Bands
df_mean = df_raw.groupby('purchase_date').quantity.mean()
df_se = df_raw.groupby('purchase_date').quantity.apply(sem).mul(1.96)
# Plot
plt.figure(figsize=(10, 6), dpi=80)
plt.ylabel("Daily Orders", fontsize=12)
x = [d.date().strftime('%Y-%m-%d') for d in df_mean.index]
plt.plot(x, df_mean, color="#c72e29", lw=2)
plt.fill_between(x, df_mean - df_se, df_mean + df_se, color="#f8f2e4")
# Decorations
# Lighten borders
plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(1)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(1)
plt.xticks(x[::6], [str(d) for d in x[::6]], fontsize=12)
plt.title("Daily Order Quantity of Brazilian Retail with Error Bands (95% confidence)",fontsize=14)
# Axis limits
s, e = plt.gca().get_xlim()
plt.xlim(s, e - 2)
plt.ylim(4, 10)
# Draw Horizontal Tick lines
for y in range(5, 10, 1):
    plt.hlines(y,
               xmin=s,
               xmax=e,
               colors='black',
               alpha=0.5,
               linestyles="--",
               lw=0.5)
plt.show()

实现效果：

3.绘制双坐标系时间序列图

实现功能：

python绘制双坐标系（双变量）时间序列图。

实现代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Import Data
df = pd.read_csv("F:\数据杂坛\datasets\economics.csv")
x = df['date']
y1 = df['psavert']
y2 = df['unemploy']
# Plot Line1 (Left Y Axis)
fig, ax1 = plt.subplots(1, 1, figsize=(12, 6), dpi=100)
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=18)
ax1.tick_params(axis='x', rotation=70, labelsize=12)
ax1.set_ylabel('Personal Savings Rate', color='#dc2624', fontsize=16)
ax1.tick_params(axis='y', rotation=0, labelcolor='#dc2624')
ax1.grid(alpha=.4)
# ax2 (right Y axis)
ax2.set_ylabel("Unemployed (1000's)", color='#01a2d9', fontsize=16)
ax2.tick_params(axis='y', labelcolor='#01a2d9')
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=18)
fig.tight_layout()
plt.show()

实现效果：

4.绘制金字塔图

实现功能：

python绘制金字塔图，一种排过序的分组水平柱状图barplot，可很好展示不同分组之间的差异，可可视化逐级过滤或者漏斗的每个阶段。

实现代码：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Read data
df = pd.read_csv("D:\数据杂坛\datasets\email_campaign_funnel.csv")
# Draw Plot
plt.figure()
group_col = 'Gender'
order_of_bars = df.Stage.unique()[::-1]
colors = [
    plt.cm.Set1(i / float(len(df[group_col].unique()) - 1))
    for i in range(len(df[group_col].unique()))
]
for c, group in zip(colors, df[group_col].unique()):
    sns.barplot(x='Users',
                y='Stage',
                data=df.loc[df[group_col] == group, :],
                order=order_of_bars,
                color=c,
                label=group)
# Decorations
plt.xlabel("$Users$")
plt.ylabel("Stage of Purchase")
plt.yticks(fontsize=12)
plt.title("Population Pyramid of the Marketing Funnel", fontsize=18)
plt.legend()
plt.savefig('C:\工作\学习\数据杂坛\素材\\0815\金字塔', dpi=300, bbox_inches = 'tight')
plt.show()

实现效果：