data[column_1].map(len).map(lambdax: x/100).plot pandas 的一个很好的功能就是链式方法(https://tomaugspurger.github.io/method-chaining)。它可以帮助你在一行中更加简单、高效地执行多个操作(.map 和.plot)。 data.apply(sum) .apply 会给一个列应用一个函数。 .applymap 会给表 (DataFrame) 中...
1%matplotlib inline2importmatplotlib.pyplot as plt3housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4,4s=housing["population"]/100, label="population", figsize=(10,7),5c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True,6sharex=False)7plt.legend()8save...
plt.figure(figsize=(12,8)) for var,plot in [('NO2 AQI',221), ('O3 AQI',222),('SO2 AQI',223),("CO AQI",224)]: plt.subplot(plot)pollutant_plot = city_poll[var] pollutant_plotTop = pollutant_plot.mean().nlargest(4).index for i in pollutant_plotTop: plot1= pollutant_plot....
df_students.plot.bar(x='Name',y='StudyHours',color='teal',figsize=(6,4)) # Get the variable to examine # Get the variable to examine # Get the variable to examine # Create a function that we can re-use defshow_density(var_data): ...
frommatplotlibimportpyplotasplt# Get the data as a Pandas dataframedata = spark.sql("SELECT Category, COUNT(ProductID) AS ProductCount \ FROM products \ GROUP BY Category \ ORDER BY Category").toPandas()# Clear the plot areaplt.clf()# Create a Figurefig = plt.figure(figsize=(12,8))#...
plt.figure(figsize=(12,8)) foriinrange(num_houses): plt.scatter(distances[i],prices[i],s=get_emoji_size(agent_levels[i]),\ c=get_emoji_color_new(prices[i]), marker='o',edgecolors='black',alpha=0.8) plt.text(distances[i],prices[i],"😊",fontsize=agent_levels[i]*10, ...
figsize:指定图表的大小。 下面是使用plot参数绘制折线图和饼图的代码示例: # 绘制折线图data.plot(x='date',y='sales',kind='line',title='Sales Trend',color='blue',figsize=(10,6))# 绘制饼图data['category'].value_counts().plot(kind='pie',title='Category Distribution',figsize=(8,8)) ...
fig = plt.figure(figsize=[12,18]) ax = fig.add_subplot(111) def basemask(cs, ax, map, shpfile): sf = shapefile.Reader(shpfile) vertices = [] codes = [] for shape_rec in sf.shapeRecords(): if shape_rec.record[0] >= 0: ...
boxplot(data=df['feature'], ax=axes[0]); sns.violinplot(data=df['feature'], ax=axes[1]); # Bar plot _, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4)) sns.countplot(x='cat_feat_1', data=df, ax=axes[0]); # can add "hue" for another cat_feat sns.countplot...
plt.figure(figsize=(10, 8)) sns.heatmap(corr, annot=True, fmt=".2f", cmap='coolwarm') plt.show() Output: fmt=".2f"ensures that numbers are formatted to two decimal places. Working with seaborn heatmaps is very easy. I hope you find the tutorial useful. ...