I have a dataframe consisting of some continuous data features. I did a kde plot of the features using seaborn kdeplot functionality which gave  me a plot as shown below :How do I interpret this visualization in order to check for things like skew in the data points,etc? Thanks in advance. 
Check for skewness in data
machine learning;python;visualization;data cleaning
IIUC you can use [DataFrame.hist()] method:import matplotlibimport matplotlib.pyplot as pltmatplotlib.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]: dfOut[44]:    a  b  c  d0   3  0  2  51   8  7  6  62   6  4  5  73   4  4  0  64   5  6  0  25   0  0  4  86   7  6  7  47   7  6  6  28   6  5  9  49   6  3  6  910  7  9  7  611  9  3  5  612  9  4  7  013  2  8  8  814  0  8  4  715  1  5  2  416  2  6  6  417  0  3  8  118  4  1  0  419  4  4  6  8In [45]: df.skew()Out[45]:a   -0.154849b   -0.239881c   -0.660912d   -0.376480dtype: float64In [46]: df.describe()Out[46]:               a          b          c          dcount  20.000000  20.000000  20.000000  20.000000mean    4.500000   4.600000   4.900000   5.050000std     2.964705   2.521487   2.770142   2.502105min     0.000000   0.000000   0.000000   0.00000025%     2.000000   3.000000   3.500000   4.00000050%     4.500000   4.500000   6.000000   5.50000075%     7.000000   6.000000   7.000000   7.000000max     9.000000   9.000000   9.000000   9.000000