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按数据帧分组,并对每组的子地块进行迭代

subplot matplotlib numpy pandas python

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Jabbar · 技术社区 · 2 年前

我有一个数据帧。我想(在步骤1中)按“Main”列对其进行分组(创建df_M1和df_M2,并为每个创建的df制作一个子图)。在(步骤2)中,按列“Sub”对每个df(df_M1和df_M2)进行分组(以创建df_S1、df_S2、df_S3、df_S4)。然后在(步骤3)中循环每个Sub-df的列(col1和col2),将它们绘制在其中一个图上。实际上,步骤1创建两个数据帧,步骤2为步骤1中创建的每个数据帧创建四个数据帧。所以我想为(df_M1和df_M2)创建两组子图(每个子图有4个图(2*2)),为(df_S1,df_S2,df_S3,df_S4)创建每个子图。每个图都包含col1和col2折线图。列“日期”在X轴上,col1和col2的值在Y轴上。我写了以下脚本,但在定义绘制折线图的子情节时存在问题。那么,有人能告诉我我如何定义每个Sub-df的子图(轴),并在每个图上绘制每个Sub-df的coulns(col1和col2)吗?

创建示例数据帧

data = {'Date': [2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001],
        'Main': ['A','A','A','A','A','A','A','A','B','B','B','B','B','B','B','B'],
       'Sub' : ['A1','A1','A2','A2','A3','A3','A4','A4','B1','B1','B2','B2','B3','B3','B4','B4'],
       'col1' : [1,2,4,5,1,2,6,4,8,5,7,2,4,5,1,2],
       'col2' : [5,6,1,4,5,4,5,1,5,4,5,6,4,5,8,4]}

df = pd.DataFrame(data)
df_M = [x for _, x in df.groupby(['Main'])]
for i in df_M:
    fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(12,6), 
sharex=True,linewidth=1, edgecolor='black')
    df_S = [z for _, z in i.groupby(['Sub'])]
    for j in df_S:
        for col in j.columns.values[3:5]:
            ax.plot(j[col])
            plt.show()

1 回复 | 直到 2 年前

fam-woodpecker 2 年前

您需要在 axes 对象,即 axes[0,0] 在子图中2x2网格的第一行和第一列的轴上绘制。

编辑添加日期作为x轴

import matplotlib.pyplot as plt
import pandas as pd

data = {
    "Date": [2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,2000,2001,],
    "Main": ["A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B",],
    "Sub": ["A1","A1","A2","A2","A3","A3","A4","A4","B1","B1","B2","B2","B3","B3","B4","B4",],
    "col1": [1, 2, 4, 5, 1, 2, 6, 4, 8, 5, 7, 2, 4, 5, 1, 2],
    "col2": [5, 6, 1, 4, 5, 4, 5, 1, 5, 4, 5, 6, 4, 5, 8, 4],
}

df = pd.DataFrame(data)
df_M = [x for _, x in df.groupby(["Main"])]


for groupName, groupdf in df.groupby(["Main"]):
    fig, axes = plt.subplots(
        nrows=2, ncols=2, figsize=(12, 6), sharex=True, linewidth=1, edgecolor="black"
    )

    for idx, (subGroupName, subGroupdf) in enumerate(groupdf.groupby(["Sub"])):
        row = 0 if idx < 2 else 1
        col = idx % 2
        for plottingCol in subGroupdf.columns.values[3:5]:
            axes[row, col].plot(subGroupdf.Date, subGroupdf[plottingCol])

    plt.show()

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