lakshay-arora

import pandas as pd
import gmplot
data = pd.read_csv('3D_spatial_network.csv')
data.head()

lakshay-arora

# latitude and longitude list 
latitude_list = data['LATITUDE'] 
longitude_list = data['LONGITUDE'] 

# center co-ordinates of the map 
gmap = gmplot.GoogleMapPlotter( 56.730876,9.349849,9)

# plot the co-ordinates on the google map 
gmap.scatter( latitude_list, longitude_list, '# FF0000', size = 40, marker = True) 

lakshay-arora

import pandas as pd 
import category_encoders as ce 

# create a Dataframe 
data = pd.DataFrame({ 'gender' : ['Male', 'Female', 'Male', 'Female', 'Female'],
                      'class' : ['A','B','C','D','A'],
                      'city' : ['Delhi','Gurugram','Delhi','Delhi','Gurugram'] }) 
                                                                                      
data.head()

lakshay-arora

# One Hot Encoding 
# create an object of the One Hot Encoder 

ce_OHE = ce.OneHotEncoder(cols=['gender','city']) 

# transform the data 
data = ce_OHE.fit_transform(data) 
data.head()

lakshay-arora

import pandas as pd
from tqdm._tqdm_notebook import tqdm_notebook
from pysal.lib.cg import harcdist
tqdm_notebook.pandas()
data = pd.read_csv('3D_spatial_network.csv')
data.head()

lakshay-arora

# calculate the distance of each data point from # (Latitude, Longitude) = (58.4442, 9.3722) 

def calculate_distance(x): 
   return harcdist((x['LATITUDE'],x['LONGITUDE']),(58.4442, 9.3722)) 
   
data['DISTANCE'] = data.progress_apply(calculate_distance,axis=1)

lakshay-arora

import pandas as pd 
import pandas_profiling 

# read the dataset 
data = pd.read_csv('add-your-data-here') 
pandas_profiling.ProfileReport(data)

lakshay-arora

import pandas as pd 

data = pd.read_excel('sales-data.xlsx') 
data.head()

lakshay-arora

data['date'] = pd.to_datetime(data['date'])

lakshay-arora

data.set_index('date').groupby('name')["ext price"].resample("M").sum()