Provide a summary of the dataset (You may have different numbers than mine as the first and last days can be exclusive or inclusive.)Analyze the impacts of the hurricane and provide a report of your...

2/27/23, 1:04 PM 311data - Jupyter Notebook
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id location_type incident_address city borough latitude longitude created_date closed_date agency agency_name c
0 23531046 NaN 111 LAWRENCESTREET BROOKLYN BROOKLYN 40.69283 -73.98623 7/1/2012 3:48
7/26/2012
12:29 TLC
Taxi and
Limousine
Commission
1 23534651 Street 5614 BROADWAY BRONX BRONX 40.87983 -73.90414 7/2/201214:05
7/17/2012
12:35 DOT
Department of
Transportation Brok
2 23535556 NaN NaN NEW YORK MANHATTAN 40.73986 -73.97774 7/2/201215:28
7/2/2012
21:00 DEP
Department of
Environmental
Protection
3 23536817 NaN 41-15 45 STREET SUNNYSIDE QUEENS 40.74641 -73.91898 7/2/201211:20
7/6/2012
0:00 DOB
Department of
Buildings
4 23536826 NaN 140 EAST 46STREET NEW YORK MANHATTAN 40.75378 -73.97372 7/2/2012 9:59
9/19/2012
0:00 DOB
Department of
Buildings
... ... ... ... ... ... ... ... ... ... ... ...
815533 24696094 RESIDENTIALBUILDING
318 ROCHESTER
AVENUE BROOKLYN BROOKLYN 40.66721 -73.92850
12/31/2012
0:00
1/4/2013
0:00 HPD
Department of
Housing
Preservation
and Develop...
PAI
815534 24696785 RESIDENTIALBUILDING
71 STUYVESANT
AVENUE BROOKLYN BROOKLYN 40.69350 -73.93415
12/31/2012
0:00
1/3/2013
0:00 HPD
Department of
Housing
Preservation
and Develop...
815535 24702974 NaN 259 MELROSESTREET BROOKLYN BROOKLYN 40.70190 -73.92982
12/27/2012
13:42
12/28/2012
0:00 DOB
Department of
Buildings Constru
815536 26816391 ResidentialBuilding/House
315 WEST 33
STREET NEW YORK MANHATTAN 40.75183 -73.99465
10/16/2012
2:30
10/16/2012
2:44 NYPD
New York City
Police
Department
Nois
815537 26827724 Street Address 89-25 PARSONSBOULEVARD JAMAICA QUEENS 40.70547 -73.80147
10/16/2012
15:28
10/16/2012
16:40 FDNY
Bureau of Fire
Prevention -
Highrise Unit -
In...
Fire S
815538 rows × 14 columns
#Library import
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns
from datetime import datetime
sns.set(font_scale=2)

#Loading of Data
data=pd.read_csv('311data.csv')
data
#Capitalize whole content of Data frame
#because there are instances where same data is written in capital or lower case.
data = data.apply(lambda x: x.astype(str).str.upper())

#Replacement of rebundent data1
data.loc[data["complaint_type"] == "DERELICT VEHICLE", "complaint_type"] = "DERELICT VEHICLES"
#cleansing of data
#converting created date coloumn's date time format to date format
data['created_date'] = pd.to_datetime(data['created_date']).dt.date


2/27/23, 1:04 PM...