Python 散点图误差

我正试着想象这个世界。我已将数据复制并粘贴到txt文件中，并使用以下代码将其转换为

.xlsx

文件：

with open('Galton_height_Data.txt','r') as f:

data = []

for i in f:
    z = i.strip('\n')
    z = z.split('\t')
    data.append(z)

import pandas as pd

df = pd.DataFrame( data[1:], columns = data[0] )

df.to_excel('Galton_Height.xlsx')

现在，我想用散点图来可视化数据。我试图通过以下方式想象母亲的身高与孩子的身高：

import pandas as pd
import matplotlib.pyplot as plt
import seaborn


df = pd.read_excel("Galton_Height.xlsx")

ax = df.plot( kind = 'scatter' , x = df['Mother'], y = df['Height'])

返回以下错误：

`KeyError: '[ 67.   67.   67.   67.   66.5  66.5  66.5  66.5  64.   64.   64.   64.\n  64.   64.   64.   58.5  58.5  58.5  58.5  58.5  58.5  68.   68.   68.\n  68.   68.   68.   68.   66.5  66.5  66.5  66.   65.5  62.   62.   62.\n  62.   62.   62.   62.   62.   61.   67.   67.   66.5  66.5  66.5  65.\n  65.   65.   65.   65.   65.   65.   65.   65.   64.5  64.5  64.5  64.5\n  64.5  64.5  64.   64.   64.   63.   69.   69.   69.   69.   69.   69.\n  69.   69.   68.   68.   68.   67.   67.   67.   65.   65.   65.   65.\n  65.   65.   65.   65.5  64.   64.   63.   63.   63.   63.   63.   63.\n  63.   63.   63.   63.   63.   63.   63.   63.   63.5  63.5  63.5  62.\n  62.   62.   62.   62.   62.   62.   62.   62.   62.   62.   62.   62.\n  62.   62.   62.   62.   61.   69.   69.   69.   69.   69.   67.   67.\n  67.   67.   66.   66.   66.   66.   66.   66.   66.   66.   66.   66.\n  66.   66.   66.   66.   66.   66.   66.   65.5  65.5  65.5  65.5  65.5\n  65.5  65.5  65.5  65.5  65.   65.   65.   65.   65.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.5  64.5  64.5  64.5  64.   64.   64.\n  64.5  64.5  64.5  64.5  64.5  64.5  64.5  63.   63.   63.5  63.5  63.5\n  63.5  63.5  63.   63.   63.   63.   63.   63.   63.   63.   63.   63.\n  63.   63.   63.   62.   62.   62.   62.   62.   62.   62.   62.   62.\n  62.   62.5  62.5  62.5  62.5  62.5  62.   62.   62.   62.   62.   62.\n  62.   61.   58.   58.   69.   69.   69.   69.   69.   69.   69.   69.\n  69.   69.   68.   67.   67.   67.   67.   67.   67.   66.5  66.5  66.5\n  66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.5  65.   65.   65.   65.\n  65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  64.7  64.7  64.7  64.7  64.7  64.7  64.7  64.   64.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.   64.2  64.2  64.2  64.2  64.2  64.\n  64.   64.   64.   64.   64.   64.   64.   64.5  64.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.   64.   64.   64.   63.7  63.7  63.7\n  63.7  63.7  63.7  63.7  63.7  63.   63.   63.   63.   63.   63.5  63.5\n  63.5  63.5  63.   63.   63.   63.   63.   63.   63.   63.   62.   62.\n  62.   62.   62.   62.   62.   62.   62.7  62.7  62.7  62.7  62.7  62.7\n  62.7  62.   62.   62.   61.   61.   60.   60.   60.   60.   60.   60.\n  58.5  58.5  58.5  58.   58.   58.   58.   58.   68.5  68.5  68.5  68.5\n  68.5  68.5  68.5  68.5  68.5  68.5  67.   66.   66.   66.   66.   66.\n  66.   66.   66.   66.   66.   66.   66.7  66.7  66.7  66.7  66.7  66.7\n  66.   66.   66.   66.   66.   66.   66.5  66.5  66.5  66.5  66.5  66.5\n  66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.   66.   66.\n  66.   66.   66.   66.   66.   66.   66.   66.   66.   66.   66.   66.\n  66.   65.   65.   65.   65.   65.   65.   65.   64.5  64.5  64.5  64.5\n  64.5  64.5  64.5  64.   64.   64.   64.   63.   63.   63.   63.   63.\n  63.   63.   63.   63.   63.   63.5  63.5  63.5  63.5  63.5  63.5  63.5\n  63.5  63.5  63.5  62.   62.   62.   62.   62.   62.   62.   62.   62.\n  62.5  62.5  62.5  62.5  62.5  62.5  62.5  62.5  62.   62.   62.   62.\n  61.   61.   61.   61.   61.   61.   61.   61.   61.   61.   61.   61.\n  61.   61.   60.   60.   60.   60.   60.   60.   60.   60.5  70.5  70.5\n  67.   67.   67.   66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.5  66.5\n  66.5  65.   65.   65.5  65.5  65.5  65.5  65.5  65.5  65.5  65.5  65.5\n  65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  64.   64.   64.   64.   64.   64.   64.   64.   64.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.   64.   64.5  64.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.   63.   63.   63.   63.   63.   63.\n  63.   63.   63.5  63.5  63.5  63.5  63.   63.   63.   63.   63.   63.\n  63.   63.   63.   63.   63.   63.   63.   63.   63.   63.   63.   63.\n  63.   63.5  63.   63.5  63.5  63.5  63.5  63.5  62.5  62.   62.   62.5\n  61.   61.   61.   61.   61.   60.2  60.   60.   60.   60.   60.   60.\n  60.   60.   60.   60.   60.   59.   59.   59.   59.   59.   59.   59.\n  59.   59.   59.   59.   66.2  66.2  66.2  66.2  66.2  66.5  65.   65.\n  65.   65.   65.   65.   65.5  65.5  65.5  65.5  65.5  65.5  65.5  65.5\n  65.5  65.   65.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  65.   65.   65.   64.   64.   64.   64.   63.5  63.5  63.5  63.5  63.5\n  63.5  63.5  63.5  63.   63.   63.   62.   62.   62.   62.   62.   61.\n  67.   67.   67.   67.   67.   67.   67.   67.   67.   67.   67.   67.\n  67.   67.   67.   67.   67.   66.   66.   66.   66.   66.   66.   66.\n  66.   66.   66.   66.   65.   65.   65.   65.   65.   65.   65.   65.\n  65.5  65.5  65.5  65.5  65.5  63.   63.5  63.5  63.   63.   63.   63.\n  63.   63.   62.5  62.5  62.5  62.5  62.5  62.5  62.5  61.5  60.   60.\n  60.   60.   60.   59.   59.   59.   59.   59.   59.   59.   59.   59.\n  59.   59.   59.   59.   59.   59.   67.   67.   67.   67.   67.   66.\n  66.   66.   66.   65.   65.   65.   65.   65.   65.   65.   65.   65.\n  65.5  65.5  65.   65.   65.   65.   65.   65.   64.   64.   64.   64.\n  64.   64.   63.   63.   63.   63.   63.   63.   63.   63.   63.   60.\n  60.   60.   60.   60.   64.   64.   64.   64.   64.   64.   64.   64.\n  64.   64.   64.   64.   64.   64.   63.   60.   60.   66.   66.   66.\n  63.   63.   65.   65.   65.   65.   65.   65.   65.   65. ] not in index' 

`

这是母亲的身高数据。看起来有些值有一个“\n”，但我想我在转换为xlsx文件时已经解决了这个问题

---

发生了什么事？

为了得到它的价值，您可以使用

pandas

解析器来读取该文件

df = pd.read_csv('Galton_height_Data.txt', delim_whitespace=True)

对于绘图，将列名传递到方法中

df.plot(kind='scatter', x='Mother', y='Height')