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datasetsorders_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datasetseconomics.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datasetsemail_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815金字塔", dpi=300, bbox_inches = "tight") plt.show()
实现效果:
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原文地址:https://blog.csdn.net/sinat_41858359/article/details/125996714