python - TypeError: dtype 对象的图像数据无法转换为浮点数 - 使用 Seaborn 的 HeatMap Plot 问题

Question

我收到错误：

TypeError: Image data of dtype object cannot be converted to float

当我尝试heapmap在下面的代码中运行该函数时：

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Read the data 
df = pd.read_csv("gapminder-FiveYearData.csv")
print(df.head(10))

# Create an array of n-dimensional array of life expectancy changes for countries over the years.
year = ((np.asarray(df['year'])).reshape(12,142))
country = ((np.asarray(df['country'])).reshape(12,142))

print(year)
print(country)

# Create a pivot table
result = df.pivot(index='year',columns='country',values='lifeExp')
print(result)

# Create an array to annotate the heatmap
labels = (np.asarray(["{1:.2f} \n {0}".format(year,value)
                      for year, value in zip(year.flatten(),
                                               country.flatten())])
         ).reshape(12,142)

# Define the plot
fig, ax = plt.subplots(figsize=(15,9))

# Add title to the Heat map
title = "GapMinder Heat Map"

# Set the font size and the distance of the title from the plot
plt.title(title,fontsize=18)
ttl = ax.title
ttl.set_position([0.5,1.05])

# Hide ticks for X & Y axis
ax.set_xticks([]) 
ax.set_yticks([]) 

# Remove the axes
ax.axis('off')

# Use the heatmap function from the seaborn package
hmap = sns.heatmap(result,annot=labels,fmt="",cmap='RdYlGn',linewidths=0.30,ax=ax)

# Display the Heatmap
plt.imshow(hmap)

这是 CSV 文件的链接。

活动的目标是

1. data 文件是包含 6 列的数据集，即：国家、年份、流行、大陆lifeExp和gdpPercap.

2. 创建一个数据透视表数据框，x 轴为年份，y 轴为国家，并lifeExp在单元格内填充。

3. 使用 seaborn 为刚刚创建的数据透视表绘制热图。

标签： pythonnumpymatplotlibseabornheatmap

Answer 1

感谢您为此问题提供数据。我相信您的 typeError 来自labels您的代码为注释创建的数组。基于函数的内置注释属性，我实际上认为您不需要这些额外的工作，它会以一种在绘图时出错的方式修改您的数据。

我尝试重新编写您的项目以生成显示 和 的数据透视表的country热year图lifeExp。我还假设保持这个数字对你很重要 a float。

import numpy as np
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt

## UNCHANGED FROM ABOVE **
# Read in the data
df = pd.read_csv('https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv')
df.head()

## ** UNCHANGED FROM ABOVE ** 
# Create an array of n-dimensional array of life expectancy changes for countries over the years.
year = ((np.asarray(df['year'])).reshape(12,142))
country = ((np.asarray(df['country'])).reshape(12,142))

print('show year\n', year)
print('\nshow country\n', country)

# Create a pivot table
result = df.pivot(index='country',columns='year',values='lifeExp')
# Note: This index and columns order is reversed from your code. 
# This will put the year on the X axis of our heatmap
result

我删除了labels代码块。功能注意事项sb.heatmap：

• 我曾经plt.cm.get_cmap()限制映射中的颜色数量。如果您想使用整个颜色图谱，只需将其删除并包括您最初的方式。
• fmt= "f", this if for float, 你的lifeExp值。
• cbar_kws- 您可以使用它来调整颜色条的大小、标签和方向。

# Define the plot - feel free to modify however you want
plt.figure(figsize = [20, 50])

# Set the font size and the distance of the title from the plot
title = 'GapMinder Heat Map'
plt.title(title,fontsize=24)

ax = sb.heatmap(result, annot = True, fmt='f', linewidths = .5,
                 cmap = plt.cm.get_cmap('RdYlGn', 7), cbar_kws={
                     'label': 'Life Expectancy', 'shrink': 0.5})

# This sets a label, size 20 to your color bar
ax.figure.axes[-1].yaxis.label.set_size(20)
plt.show()

有限的屏幕截图，只有 b/c 情节如此之大 ，情节底部的另一个显示年份轴，在我的浏览器上稍微放大。