text_plain_output_1.png
import matplotlib.pyplot as plt

import mglearn

import pandas

import seaborn

import seaborn
import matplotlib.pyplot as plt
import pandas
df_pandas = pandas.read_csv('../input/voicegender/voice.csv')

# performing the visualization in pandas Data Frame 
male = df_pandas.loc[df_pandas['label']=='male']
female = df_pandas.loc[df_pandas['label']=='female']
fig, axes = plt.subplots(10, 2, figsize=(10,20))
ax = axes.ravel()
for i in range(20):
    ax[i].hist(male.iloc[:,i], bins=20, color=mglearn.cm3(0), alpha=.5)
    ax[i].hist(female.iloc[:, i], bins=20, color=mglearn.cm3(2), alpha=.5)
    ax[i].set_title(list(male)[i])
    ax[i].set_yticks(())
    ax[i].set_xlabel("Feature magnitude")
    ax[i].set_ylabel("Frequency")
    ax[i].legend(["male", "female"], loc="best")

fig.tight_layout()

ND = df_pandas.drop(['sfm', 'kurt', 'meandom', 'meanfreq', 'dfrange', 'modindx'], axis=1)
ND