如何使用 python 可视化来自 dataframe 的分组数据

Question

After grouping two columns of data in my dataframe, I obtained a small table of integers whose image I've attached below (it has been given as a link since I am very new at posting in stack overflow).

Please click here for the image of the data

This was the code used for grouping:

count = x_train.groupby(['bool_loc', 'target']).size() 

I am trying to visualize this data (type int64) using python and thought that maybe a histogram with two categories 0 and 1 (for column 'bool_loc') and each category having two bars (for column 'target') with their heights representing frequency would be a good way to do so. I tried like this:

# create figure and axis
fig, ax = plt.subplots()
# plot histogram
ax.hist(count)
# set title and labels
ax.set_title('Relation Between Location Data Presence and Disaster Tweets')
ax.set_xlabel('Location Data Presence')
ax.set_ylabel('Frequency of Tweets')

The histogram I obtained:

Image of obtained histogram

It seems that the frequency data has been plotted along the x-axis (it should be on the y-axis) instead of the data in 'bool_loc'. I would really appreciate some guidance in this and welcome other visualization techniques.

** Please tell me if this question needs to be made clearer

Answer 1

I tried to visualize histograms based on the shape of your dataframe. Here is the result: 2 histograms with 2 bins

I'm not sure if this complies with your data input, as I simply made similar dataframe to the one in your post. Probably you have it done differently.

The code is below:

import pandas as pd

import matplotlib
import numpy as np
import matplotlib.pyplot as plt

# make dataframe
arrays = [[0, 0, 1, 1],
          [0, 1, 0, 1]]
data = [1458, 1075, 2884, 2196] 
df = pd.DataFrame(data, index=arrays, columns=['frequency'])

# get data from DF series
y1 = df.loc[0,'frequency'].to_list()
y2 = df.loc[1,'frequency'].to_list()

# get data arrays
arr1 = [0] * y1[0] + [1] * y1[1]
arr2 = [0] * y2[0] + [1] * y2[1]

# set matplotlib plot
fig, ax = plt.subplots()

# plot histogram
num_bins = 2
ax.hist([arr1, arr2], num_bins, density=False, label=['bool_loc 0', 'bool_loc 1'])
plt.legend(loc='upper right')
plt.show()

Answer 2

It seems like you want us to write code for you. Did you try it before if yes include your code in question. It would be easy for us to understand and so we can modify for you corrct version of code. Thanks

I don't know much about your date but still try this

print (data.target.value_counts(normalize=True).plot(kind='bar'))

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