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：
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