text_plain_output_1.png
from pylab import rcParams

import pandas as pd #for data wrangling

import seaborn as sns #data visualization

noise_complaints_data = pd.read_csv('../input/Noise_Complaints.csv')

noise_complaints_data.isna().sum()

noise_complaints_data.drop(['Status', 'Due Date', 'Agency', 'Agency Name', 'Landmark', 'Facility Type', 'Status', 'Due Date', 'Resolution Description', 'Community Board', 'Park Facility Name', 'Park Borough', 'Vehicle Type', 'Taxi Company Borough', 'Taxi Pick Up Location', 'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp', 'Bridge Highway Segment'], axis=1)

comp_desc = noise_complaints_data['Descriptor'].value_counts()
comp_desc

comp_desc_df = comp_desc.to_frame().reset_index()

comp_desc_df.rename(columns={'index': 'Description'}, inplace=True)
comp_desc_df.rename(columns={'Descriptor': 'Count'}, inplace=True)

from pylab import rcParams
rcParams['figure.figsize'] = (10, 3)
sns.barplot(x='Count', y='Description', data=comp_desc_df.head(10)).set_title('Top 10 NYC Noise Compaint Descriptors 2018')