Distribution probabilities for each column data frame, in one plot

Question

I am creating probability distributions for each column of my data frame by distplot from seaborn library sns.distplot(). For one plot I do

x = df['A']
sns.distplot(x);

I am trying to use the FacetGrid & Map to have all plots for each columns at once in this way. But doesn't work at all.

  g = sns.FacetGrid(df, col = 'A','B','C','D','E')
  g.map(sns.distplot())

Answer 1

I think you need to use melt to reshape your dataframe to long format, see this MVCE:

df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
dfm = df.melt(var_name='columns')
g = sns.FacetGrid(dfm, col='columns')
g = (g.map(sns.distplot, 'value'))

Output:

Answer 2

You're getting this wrong on two levels.

• Python syntax.
  FacetGrid(df, col = 'A','B','C','D','E') is invalid, because col gets set to A and the remaining characters are interpreted as further arguments. But since they are not named, this is invalid python syntax.

• Seaborn concepts.

  • Seaborn expects a single column name as input for the col or row argument. This means that the dataframe needs to be in a format that has one column which determines to which column or row the respective datum belongs.

  • You do not call the function to be used by map. The idea is of course that map itself calls it.

Solutions:

• Loop over columns:

  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
  
  fig, axes = plt.subplots(ncols=5)
  for ax, col in zip(axes, df.columns):
      sns.distplot(df[col], ax=ax)
  
  plt.show()
  

• Melt dataframe

  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
  
  g = sns.FacetGrid(df.melt(), col="variable")
  g.map(sns.distplot, "value")
  
  plt.show()
  

Answer 3

I think the easiest approach is to just loop the columns and create a plot.

import numpy as np
improt pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
for col in df.columns:
    hist = df[col].hist(bins=10)
    print("Plotting for column {}".format(col))
    plt.show()