text_plain_output_1.png
import matplotlib.pyplot as plt

import os

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()

health_df1.duplicated().sum()

health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()

health_df1 = health_df1.drop(['Indicator', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods', 'Notes'], axis=1)

plt.figure(figsize=(15,5))
ax = sns.barplot(x='Indicator Category',y='Value', data=health_df1)
ax.set_title('Indicator Category vs Value')
plt.xlabel("Indicator Category")
plt.ylabel('Value')
plt.show(ax)

plt.figure(figsize=(15,5))
ax = sns.barplot(x='Indicator Category',y='Value', hue='Gender',data=health_df1)
ax.set_title('Indicator Category vs Value by Gender')
plt.xlabel("Indicator Category")
plt.ylabel('Value')
plt.show(ax)

plt.figure(figsize=(15, 5))
ax = sns.barplot(x='State', y='Value', hue='Gender', data=health_df1)
ax.set_title('State vs Value by Gender')
plt.xlabel('State')
plt.ylabel('Value')
plt.show(ax)