IIUC you can use [DataFrame.hist()] method:
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
matplotlib.style.use('ggplot')
df = pd.DataFrame(np.random.randint(0,10,(20,4)),columns=list('abcd'))
df.hist(alpha=0.5, figsize=(16, 10))
Result:

Data:
In [44]: df
Out[44]:
a b c d
0 3 0 2 5
1 8 7 6 6
2 6 4 5 7
3 4 4 0 6
4 5 6 0 2
5 0 0 4 8
6 7 6 7 4
7 7 6 6 2
8 6 5 9 4
9 6 3 6 9
10 7 9 7 6
11 9 3 5 6
12 9 4 7 0
13 2 8 8 8
14 0 8 4 7
15 1 5 2 4
16 2 6 6 4
17 0 3 8 1
18 4 1 0 4
19 4 4 6 8
In [45]: df.skew()
Out[45]:
a -0.154849
b -0.239881
c -0.660912
d -0.376480
dtype: float64
In [46]: df.describe()
Out[46]:
a b c d
count 20.000000 20.000000 20.000000 20.000000
mean 4.500000 4.600000 4.900000 5.050000
std 2.964705 2.521487 2.770142 2.502105
min 0.000000 0.000000 0.000000 0.000000
25% 2.000000 3.000000 3.500000 4.000000
50% 4.500000 4.500000 6.000000 5.500000
75% 7.000000 6.000000 7.000000 7.000000
max 9.000000 9.000000 9.000000 9.000000
DataFrame.skew()
andDataFrame.kurtosis()
methods. This superimposed plot is more confusing than informative. $\endgroup$df.variable.hist()
. $\endgroup$