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I have a dataframe with this kind of structure:

ID | Size | Value
1  | 1    | 10
2  | 2    | 20
3  | 1    | 10
4  | 1    | 10
5  | 1    | 15 
6  | 1    | 12
7  | 1    | 20

I want to create bar plot of the distribuition of column "Values" when the "Size" column is equal to 1.

With the current example:

  1. the x-axis should have: 10, 12, 15 and 20
  2. the y-axis should have 1 (for the 12, 15 and 20) and 3 for the 10

I don't have a lot of code. Basically I just created a new dataframe with only the rows where Size=1 and then performed value_counts():

data1 = data.loc[(data['Size'] == 1)]
count = data1['Value'].value_counts()

The variable count now has a Series object with a Value and a counter.

How can I split the value of count in order to have two lists (values and counters) to send to the plotting code?

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2 Answers 2

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What you are looking for is a histogram. It plots the distribution of a given series of values. So, you can just extract the values that you want to plot from your dataframe and then use matplotlib's histogram function. Here is the code:

import matplotlib.pyplot as plt
to_plot = data.loc[df['Size']==1]['Value']
plt.hist(to_plot)
plt.show()

This is the output I get, note that I haven't done any formatting, this is just the raw output. enter image description here

The above option is more flexible. But in your case you can also use the following:

import matplotlib.pyplot as plt
to_plot = data.loc[df['Size']==1]['Value']
to_plot.value_counts().plot.bar()

enter image description here

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You can use hist from matplotlib library

import matplotlib.pyplot as plt
plt.hist( data['Value'] )
plt.show()
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