熊猫直方图用kde？

Question

I have a Pandas dataframe ( Dt ) like this:

  Pc     Cvt      C1    C2    C3    C4    C5    C6    C7    C8    C9   C10 
   0       1       2  0.08  0.17  0.16  0.31  0.62  0.66  0.63  0.52  0.38   
   1       2       2  0.09  0.15  0.13  0.49  0.71  1.28  0.42  1.04  0.43   
   2       3       2  0.13  0.24  0.22  0.17  0.66  0.17  0.28  0.11  0.30  
   3       4       1  0.21  0.10  0.23  0.08  0.53  0.14  0.59  0.06  0.53  
   4       5       1  0.16  0.21  0.18  0.13  0.44  0.08  0.29  0.12  0.52  
   5       6       1  0.14  0.14  0.13  0.20  0.29  0.35  0.40  0.29  0.53  
   6       7       1  0.21  0.16  0.19  0.21  0.28  0.23  0.40  0.19  0.52   
   7       8       1  0.31  0.16  0.34  0.19  0.60  0.32  0.56  0.30  0.55  
   8       9       1  0.20  0.19  0.26  0.19  0.63  0.30  0.68  0.22  0.58  
   9      10       2  0.12  0.18  0.13  0.22  0.59  0.40  0.50  0.24  0.36  
   10     11       2  0.10  0.10  0.19  0.17  0.89  0.36  0.65  0.23  0.37  
   11     12       2  0.19  0.20  0.17  0.17  0.38  0.14  0.48  0.08  0.36  
   12     13       1  0.16  0.17  0.15  0.13  0.35  0.12  0.50  0.09  0.52   
   13     14       2  0.19  0.19  0.29  0.16  0.62  0.19  0.43  0.14  0.35   
   14     15       2  0.01  0.16  0.17  0.20  0.89  0.38  0.63  0.27  0.46   
   15     16       2  0.09  0.19  0.33  0.15  1.11  0.16  0.87  0.16  0.29  
   16     17       2  0.07  0.18  0.19  0.15  0.61  0.19  0.37  0.15  0.36   
   17     18       2  0.14  0.23  0.23  0.20  0.67  0.38  0.45  0.27  0.33   
   18     19       1  0.27  0.15  0.20  0.10  0.40  0.05  0.53  0.02  0.52   
   19     20       1  0.12  0.13  0.18  0.22  0.60  0.49  0.66  0.39  0.66  
   20     21       2  0.15  0.20  0.18  0.32  0.74  0.58  0.51  0.45  0.37
   .
   .
   .

From this i want to plot an histogram with kde for each column from C1 to C10 in an arrange just like the one that i obtain if i plot it with pandas,

 Dt.iloc[:,2:].hist()

But so far i've been not able to add the kde in each histogram; i want something like this:

Any ideas on how to accomplish this?

Answer 1

You want to first plot your histogram then plot the kde on a secondary axis.

Minimal and Complete Verifiable Example MCVE

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

df = pd.DataFrame(np.random.randn(1000, 4)).add_prefix('C')

k = len(df.columns)
n = 2
m = (k - 1) // n + 1
fig, axes = plt.subplots(m, n, figsize=(n * 5, m * 3))
for i, (name, col) in enumerate(df.iteritems()):
    r, c = i // n, i % n
    ax = axes[r, c]
    col.hist(ax=ax)
    ax2 = col.plot.kde(ax=ax, secondary_y=True, title=name)
    ax2.set_ylim(0)

fig.tight_layout()

---

How It Works

• Keep track of total number of subplots

   k = len(df.columns) 

• n will be the number of chart columns. Change this to suit individual needs. m will be the calculated number of required rows based on k and n

   n = 2 m = (k - 1) // n + 1 

• Create a figure and array of axes with required number of rows and columns.

   fig, axes = plt.subplots(m, n, figsize=(n * 5, m * 3)) 

• Iterate through columns, tracking the column name and which number we are at i . Within each iteration, plot.

   for i, (name, col) in enumerate(df.iteritems()): r, c = i // n, i % n ax = axes[r, c] col.hist(ax=ax) ax2 = col.plot.kde(ax=ax, secondary_y=True, title=name) ax2.set_ylim(0) 

• Use tight_layout() as an easy way to sharpen up the layout spacing

   fig.tight_layout() 

Answer 2

Here is a pure seaborn solution, using FacetGrid.map_dataframe as explained here .

Stealing the example from @piRSquared:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000, 4)).add_prefix('C')

Get the data in the required format:

df = df.stack().reset_index(level=1, name="val")

Result:

  level_1       val
0      C0  0.879714
0      C1 -0.927096
0      C2 -0.929429
0      C3 -0.571176
1      C0 -1.127939

Then:

import seaborn as sns

def distplot(x, **kwargs):
    ax = plt.gca()
    data = kwargs.pop("data")
    sns.distplot(data[x], ax=ax, **kwargs)

g = sns.FacetGrid(df, col="level_1", col_wrap=2, size=3.5)
g = g.map_dataframe(distplot, "val")

You can adjust col_wrap as needed.