Leisure Places In India


        
        data-science


        
        machine-learning


        
        project

4 min read.

Goals

show places of leisure in a map
make observations about the places and their distribution
explore the most widely available leisure type in india

import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import ast
import plotly.express as px
import geopandas as gpd

le = pd.read_csv("../input/buildings-amenities-all-over-india/leisure.csv")

le.head()

	Unnamed: 0	name	leisure	longitude-lattitude	All_tags
0	249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)	{'name': 'DLF Golf Links Golf Course', 'barrie...
1	250737365	NaN	park	(80.23786640000002, 13.04278489999996)	{'leisure': 'park'}
2	250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)	{'name': 'Yoga Centre', 'leisure': 'sports_cen...
3	280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)	{'name': 'Black Thunder', 'leisure': 'water_pa...
4	280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)	{'name': 'Ootacamund Gymkhana Golf Course', 'l...

# remove all tags column and rename columns
le = le.drop("All_tags", axis=1)
le.columns = ["id", "name", "leisure", "lo-la"]

le.head()

	id	name	leisure	lo-la
0	249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)
1	250737365	NaN	park	(80.23786640000002, 13.04278489999996)
2	250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)
3	280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)
4	280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)

# set the ID as index
le.index = le["id"]
le = le.drop("id", axis = 1)

le.head()

	name	leisure	lo-la
id
249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)
250737365	NaN	park	(80.23786640000002, 13.04278489999996)
250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)
280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)
280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)

# check NA values
le.isna().sum()

name       27143
leisure        0
lo-la      37876
dtype: int64

the latitude and longitude are the most important columns so we will drop all rows that do not have them

le = le[le['lo-la'].notna()]
le

	name	leisure	lo-la
id
249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)
250737365	NaN	park	(80.23786640000002, 13.04278489999996)
250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)
280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)
280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)
...	...	...	...
8277782288	NaN	playground	(76.29733219999959, 10.029497999999887)
8280851413	Gothuruth muzhiris park	park	(76.21773650000003, 10.190251200000016)
8280851414	Gothuruth Muzhiris park	park	(76.21771200000003, 10.190284000000016)
8281209559	Exalt Fitness Club Gym	fitness_centre	(72.56438300000039, 23.089663400000084)
8281506191	NaN	playground	(75.54409639999994, 11.927387099999967)

5813 rows × 3 columns

fill the missing names with the word “missing”

le['name'].fillna("missing", inplace=True)

le

	name	leisure	lo-la
id
249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)
250737365	missing	park	(80.23786640000002, 13.04278489999996)
250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)
280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)
280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)
...	...	...	...
8277782288	missing	playground	(76.29733219999959, 10.029497999999887)
8280851413	Gothuruth muzhiris park	park	(76.21773650000003, 10.190251200000016)
8280851414	Gothuruth Muzhiris park	park	(76.21771200000003, 10.190284000000016)
8281209559	Exalt Fitness Club Gym	fitness_centre	(72.56438300000039, 23.089663400000084)
8281506191	missing	playground	(75.54409639999994, 11.927387099999967)

5813 rows × 3 columns

most available types of leisure places according to type

le["leisure"].value_counts()

park                        1798
playground                   773
fitness_centre               580
resort                       544
pitch                        497
sports_centre                495
fishing                      238
garden                       196
stadium                      159
swimming_pool                155
dance                         58
fitness_station               56
nature_reserve                52
water_park                    24
marina                        20
slipway                       16
beach_resort                  14
common                        13
amusement_arcade              12
track                         10
yes                           10
outdoor_seating                9
golf_course                    8
recreation_ground              6
club                           6
bandstand                      5
bowling_alley                  5
hackerspace                    5
bird_hide                      4
adult_gaming_centre            4
sauna                          4
picnic_table                   3
swimming_area                  3
firepit                        3
horse_riding                   3
cultural_centre                2
gym                            2
hot_spring                     2
indoor_play                    2
wildlife_hide                  2
spa                            2
Park in residential area       1
aquarium                       1
leisure                        1
ground                         1
Meeting_point                  1
sports_hall                    1
summer_camp                    1
social_club                    1
yoga                           1
schoolyard                     1
NITTE FOOTBALL STADIUM         1
quary                          1
yoga_centre                    1
Name: leisure, dtype: int64

lets draw a graph for an easier understanding

plt.rcParams['font.size'] = 10.0
plt.rcParams['figure.figsize'] = 20, 10
ax = sns.countplot(le['leisure'], palette="Blues_r", order=le.leisure.value_counts()[:20].index)

ax.set_title("Most Avaiable Leisure Places in India")
# rotate the names so they fit
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()

png

parks are the most common type of leisure building in india

# split coordinates
cords = list(le["lo-la"])
long = []
lat = []
for cord in cords:
    set_r = ast.literal_eval(cord)
    long.append(set_r[0])
    lat.append(set_r[1])

le["long"] = long
le["lat"] = lat

le.head()

	name	leisure	lo-la	long	lat
id
249132377	DLF Golf Links Golf Course	golf_course	(77.10471029999984, 28.45473270000001)	77.104710	28.454733
250737365	missing	park	(80.23786640000002, 13.04278489999996)	80.237866	13.042785
250979543	Yoga Centre	sports_centre	(75.8870475, 31.52995199999996)	75.887047	31.529952
280167017	Black Thunder	water_park	(76.9132247999999, 11.32635400000001)	76.913225	11.326354
280701513	Ootacamund Gymkhana Golf Course	golf_course	(76.67157809999996, 11.417312599999995)	76.671578	11.417313

# drop the old coordinates column
le = le.drop("lo-la", axis=1)

le.head()

	name	leisure	long	lat
id
249132377	DLF Golf Links Golf Course	golf_course	77.104710	28.454733
250737365	missing	park	80.237866	13.042785
250979543	Yoga Centre	sports_centre	75.887047	31.529952
280167017	Black Thunder	water_park	76.913225	11.326354
280701513	Ootacamund Gymkhana Golf Course	golf_course	76.671578	11.417313

# basic scatter plot of places
plt.scatter(x=le["long"], y=le["lat"])
plt.show()

png

above is the initial shape of the locations in the map based on their longitude and latitude, we can already see that the shape looks like india meaning there are many leisure places around the country

# create and view geopandas dataframe
gdf = gpd.GeoDataFrame(
    le, geometry=gpd.points_from_xy(le.long, le.lat))
gdf

	name	leisure	long	lat	geometry
id
249132377	DLF Golf Links Golf Course	golf_course	77.104710	28.454733	POINT (77.10471 28.45473)
250737365	missing	park	80.237866	13.042785	POINT (80.23787 13.04278)
250979543	Yoga Centre	sports_centre	75.887047	31.529952	POINT (75.88705 31.52995)
280167017	Black Thunder	water_park	76.913225	11.326354	POINT (76.91322 11.32635)
280701513	Ootacamund Gymkhana Golf Course	golf_course	76.671578	11.417313	POINT (76.67158 11.41731)
...	...	...	...	...	...
8277782288	missing	playground	76.297332	10.029498	POINT (76.29733 10.02950)
8280851413	Gothuruth muzhiris park	park	76.217737	10.190251	POINT (76.21774 10.19025)
8280851414	Gothuruth Muzhiris park	park	76.217712	10.190284	POINT (76.21771 10.19028)
8281209559	Exalt Fitness Club Gym	fitness_centre	72.564383	23.089663	POINT (72.56438 23.08966)
8281506191	missing	playground	75.544096	11.927387	POINT (75.54410 11.92739)

5813 rows × 5 columns

# set mapbox acces token (required for drawing an interactive map)
px.set_mapbox_access_token("pk.eyJ1IjoiYmxhY2tzdWFuMTkiLCJhIjoiY2twcDdtaGc4MDZ6djJvczR0Ym9sa3pqNCJ9.gNL1mxeSmDi6hfgwxz2qRA")

# generate and show points in map (its intractive!)
fig = px.scatter_geo(gdf,
                    lat=gdf.geometry.y,
                    lon=gdf.geometry.x,
                    hover_data=["name", "leisure"],
                    locationmode="country names"
                    )
fig.update_geos(fitbounds="locations") # zoom in to only india
fig.show()
# check the project source code for the interactive version of the map