为每个类创建两个列表之间的散点图答案

【问题标题】：For each class create a scatter plot between two lists为每个类创建两个列表之间的散点图
【发布时间】：2021-10-20 15:48:20
【问题描述】：

我阅读了一个 CSV 文件并创建了以下两个列表。对于每个类，我希望在 values2 和 values3 之间创建一个散点图。我想查看每个类的 values2 和 values3 之间的相关性。这可能吗？

List1=vehiclesData.groupby('Class')['values2'].apply(list)
List2=vehiclesData.groupby('Class')['values3'].apply(list)

如果你打印 List1，它会是这样的：

Compact Cars                          [2.2, 1.8, 1.8...
Large Cars                            [3.8, 3.8, 3.8...
Midsize Cars                          [2.8, 2.8, 4.0...
Midsize Station Wagons                [3.0, 3.0, 2.3...

如果你打印 List2，它会是这样的：

Compact Cars                          [19, 22, 25...
Large Cars                            [20, 18, 20...
Midsize Cars                          [19, 19, 16...
Midsize Station Wagons                [19, 18, 21...

【问题讨论】：

标签： python list pandas-groupby data-analysis scatter-plot

【解决方案1】：

有三种流行的相关性度量 - Pearson、Spearman 和 Kendall Tau 相关性。两个变量之间的散点图也有助于直观地理解相关性。以下代码将帮助您计算每个 'class' 的所有三种类型的相关性，并在 'values2' 和 'values3 之间创建类散点图em>'。

代码：

import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr, kendalltau

df = ... # pandas data frame containing the original data

# unique classes
classes = list(set(list(df.loc[:, "Class"])))

# creating space for making plots
fig, ax = plt.subplots(ncols = len(classes), figsize = (len(classes)*5, 4.5), dpi = 120)

# Table that holds class-wise correlation values
correlation_table = pd.DataFrame(columns = ["Class", "Pearson correlation",
                                            "Spearman correlation", "Kendall Tau correlation"])

# populate class-wise correlation values and create class-wise scatter plots
for i, c in enumerate(classes):

    # filter dataset by class
    sub_df = df[df["Class"]==c]

    # Pearson correlation
    p_c, _ = pearsonr(sub_df.loc[:, "values2"], sub_df.loc[:, "values3"])
    # Spearman correlation
    s_c, _ = spearmanr(sub_df.loc[:, "values2"], sub_df.loc[:, "values3"])
    # Kendall Tau correlation
    k_c, _ = kendalltau(sub_df.loc[:, "values2"], sub_df.loc[:, "values3"])
    # populate correlation values
    correlation_table = correlation_table.append({"Class": c,
                                                  "Pearson correlation": p_c,
                                                  "Spearman correlation": s_c,
                                                  "Kendall Tau correlation": k_c},
                                                 ignore_index = True)
    
    # Create scatter plot
    ax[i].scatter(sub_df.loc[:, "values2"], sub_df.loc[:, "values3"])
    ax[i].set_title(f"Class: {c}")
    ax[i].set_xlabel("values2")
    ax[i].set_ylabel("values3")

print(correlation_table)
fig.show()

【讨论】：