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
目录
- 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:
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