Answers.Body: <p>Real data is better, particularly if genes names are wanted (complicated simulation) .... anyway there you go. Simulated values under a negative binomial which are pretty realistic to the data supplied (that'll be negative binomial, just not ranked).</p>
  <pre class="lang-py prettyprint-override"><code>import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  
  np.random.seed(2)
  s1 = list(np.random.negative_binomial(1, 0.1, 500))
  s2 = list(np.random.negative_binomial(1, 0.5, 500))
  mydict = {'A':s1, 'B':s2}
  df = pd.DataFrame(mydict)
  fig, ax = plt.subplots()
  
  heights, bins = np.histogram(df['A'])
  heights2, bins2 = np.histogram(df['B'], bins=bins)
  
  width = (bins[1] - bins2[0])/3
  
  ax.bar(bins[:-1], heights, width=width, facecolor='coral')
  ax.bar(bins2[:-1]+width, heights2, width=width, facecolor='blue')
  </code></pre>
  <p><a href="https://i.sstatic.net/gYD7d.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/gYD7d.png" alt="enter image description here" /></a></p>
  <hr />
  <p>Here's horizontal bars (minor differences) with slightly different negative binomial values used ...</p>
  <pre><code># same imports as before 
  
  np.random.seed(2)
  s1 = list(np.random.negative_binomial(1, 0.1, 500))
  s2 = list(np.random.negative_binomial(1, 0.3, 500))
  mydict = {'A':s1, 'B':s2}
  df = pd.DataFrame(mydict)
  fig, ax = plt.subplots()
  
  heights, bins = np.histogram(df['A'])
  heights2, bins2 = np.histogram(df['B'], bins=bins)
  
  width = (bins[1] - bins2[0])/3
  
  ax.barh(bins[:-1], heights, width, facecolor='coral')
  ax.barh(bins2[:-1]+width, heights2, width, facecolor='blue')
  ax.invert_yaxis()
  plt.show()
  
  </code></pre>
  <p><a href="https://i.sstatic.net/Kf2qA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Kf2qA.png" alt="enter image description here" /></a></p>
  <hr />
  <p>To adapt it you just need to provide an appropriate set of values that will then be fitted to a dataframe.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-25T15:16:55.687Z
Answers.LastActivityDate: 2023-06-27T19:55:35.763Z
Answers.LastEditDate: 2023-06-27T19:55:35.763Z
Answers.UserDisplayName: M__
Body: <p>How do we plot histogram in R\Python with two variables[BC and Control] and their corresponding gene names examples is below:</p>
  <p><a href="https://i.sstatic.net/W6oID.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/W6oID.png" alt="enter image description here" /></a></p>
  <p>Thank you,</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-25T10:00:49.810Z
Comments.Text: That doesn't look like a histogram but a bar plot. To draw it with ggplot2 and `geom_col()`, check out this reference: [https://ggplot2.tidyverse.org/reference/geom_bar.html](https://ggplot2.tidyverse.org/reference/geom_bar.html)
Comments.UserDisplayName: Cloudberry
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-25T10:39:15.307Z
Comments.Text: Yeah, I was just given an example through bar plot well, thanks @Cloudberry
Comments.UserDisplayName: Mahendra Singh
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-25T22:12:33.337Z
Comments.Text: How is a bar plot an example for a histogram? They're two different kinds of plots.
Comments.UserDisplayName: Ram RS
Tags: <r><python><proteins><ggplot2>
Title: Histogram plot using R/python with two variables[BC and Control] and their corresponding gene names
ThreadText: # Histogram plot using R/python with two variables[BC and Control] and their corresponding gene names
  
  (Asked by: username_0 on 2023-03-25)
  
  How do we plot histogram in R\Python with two variables[BC and Control] and their corresponding gene names examples is below:
  
  [![enter image description here](https://i.sstatic.net/W6oID.png)](https://i.sstatic.net/W6oID.png)
  
  Thank you,
  
  **Tags:** r, python, proteins, ggplot2
  
  > **Comments:**
  > **username_1**: That doesn't look like a histogram but a bar plot. To draw it with ggplot2 and `geom_col()`, check out this reference: [https://ggplot2.tidyverse.org/reference/geom_bar.html](https://ggplot2.tidyverse.org/reference/geom_bar.html)  
  > **username_0**: Yeah, I was just given an example through bar plot well, thanks @username_1  
  > **username_2**: How is a bar plot an example for a histogram? They're two different kinds of plots.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2023-03-25. Score: 1
  
  Real data is better, particularly if genes names are wanted (complicated simulation) .... anyway there you go. Simulated values under a negative binomial which are pretty realistic to the data supplied (that'll be negative binomial, just not ranked).
  
  ```py
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  
  np.random.seed(2)
  s1 = list(np.random.negative_binomial(1, 0.1, 500))
  s2 = list(np.random.negative_binomial(1, 0.5, 500))
  mydict = {'A':s1, 'B':s2}
  df = pd.DataFrame(mydict)
  fig, ax = plt.subplots()
  
  heights, bins = np.histogram(df['A'])
  heights2, bins2 = np.histogram(df['B'], bins=bins)
  
  width = (bins[1] - bins2[0])/3
  
  ax.bar(bins[:-1], heights, width=width, facecolor='coral')
  ax.bar(bins2[:-1]+width, heights2, width=width, facecolor='blue')
  
  ```
  
  [![enter image description here](https://i.sstatic.net/gYD7d.png)](https://i.sstatic.net/gYD7d.png)
  
  ---
  
  Here's horizontal bars (minor differences) with slightly different negative binomial values used ...
  
  ```
  # same imports as before 
  
  np.random.seed(2)
  s1 = list(np.random.negative_binomial(1, 0.1, 500))
  s2 = list(np.random.negative_binomial(1, 0.3, 500))
  mydict = {'A':s1, 'B':s2}
  df = pd.DataFrame(mydict)
  fig, ax = plt.subplots()
  
  heights, bins = np.histogram(df['A'])
  heights2, bins2 = np.histogram(df['B'], bins=bins)
  
  width = (bins[1] - bins2[0])/3
  
  ax.barh(bins[:-1], heights, width, facecolor='coral')
  ax.barh(bins2[:-1]+width, heights2, width, facecolor='blue')
  ax.invert_yaxis()
  plt.show()
  
  ```
  
  [![enter image description here](https://i.sstatic.net/Kf2qA.png)](https://i.sstatic.net/Kf2qA.png)
  
  ---
  
  To adapt it you just need to provide an appropriate set of values that will then be fitted to a dataframe.
  
  
  ---
  
  