loc and iloc in Pandas

PHOTO

Thu Nov 25 2021 06:55:14 GMT+0000 (UTC)

Saved by @Sourabh ##dictionary ##pandas #defining_data_frame #csv #dataframe #square #bracket'

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out observation for Japan
print(cars.loc['JPN'])
print(cars.iloc[2])

# Print out observations for Australia and Egypt
print(cars.loc[['AUS','EG']])
print(cars.iloc[[1,6]])

COPY

With loc and iloc you can do practically any data selection operation on DataFrames you can think of. loc is label-based, which means that you have to specify rows and columns based on their row and column labels. iloc is integer index based, so you have to specify rows and columns by their integer index

Save snippets that work from anywhere online with our extensions

Comments

Pandas

@Sourabh

CSV to DataFrame (2) Square Bracket in Pandas loc and iloc in Pandas loc and iloc - part 3 Filter observations from a DataFrame based on boolean arrays: Part 1 Filter observations from a DataFrame based on boolean arrays: Part 2 Dataframe subsetting Loop over Dataframe - Pandas Loop over Dataframe - Pandas: Part 2 adding columns to pandas dataframe

##dictionary

Dictionary Manipulation

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Add italy to europe
europe['italy'] = 'rome'

# Print out italy in europe
print('italy' in europe)

# Add poland to europe
europe['poland'] = 'warsaw'

# Print europe
print(europe)

##dictionary ##update ##delete

Update,Delete Dictionary elements

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }

# Update capital of germany
europe['germany'] = 'berlin'

# Remove australia
del(europe['australia'])

# Print europe
print(europe)

##dictionary ##update ##delete

Dictionariception

# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }


# Print out the capital of France
print(europe['france']['capital'])

# Create sub-dictionary data
data = {'capital':'rome','population':59.83}

# Add data to europe under key 'italy'
europe['italy'] = data

# Print europe
print(europe)

##dictionary ##pandas #defining_data_frame

Pandas(Starting

# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

# Import pandas as pd
import pandas as pd

# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {'country':['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt'],
'drives_right':[True, False, False, False, True, True, True],
'cars_per_cap':[809, 731, 588, 18, 200, 70, 45]}

# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)

# Print cars
print(cars)



#Now this is indexed beautiful arrangement of data
import pandas as pd

# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(cars_dict)
print(cars)

# Definition of row_labels
row_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']

# Specify row labels of cars
cars.index = row_labels

# Print cars again
print(cars)

##dictionary ##pandas #defining_data_frame #csv #dataframe

CSV to DataFrame (2)

# Import pandas as pd
import pandas as pd

# Fix import by including index_col
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out cars
print(cars)

##dictionary ##pandas #defining_data_frame #csv #dataframe #square #bracket'

Square Bracket in Pandas

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out country column as Pandas Series
print(cars['country'])

# Print out country column as Pandas DataFrame
print(cars[['country']])


# Print out DataFrame with country and drives_right columns
print(cars[['country','drives_right']])

##dictionary ##pandas #defining_data_frame #csv #dataframe #square #bracket'

loc and iloc in Pandas

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out observation for Japan
print(cars.loc['JPN'])
print(cars.iloc[2])

# Print out observations for Australia and Egypt
print(cars.loc[['AUS','EG']])
print(cars.iloc[[1,6]])

##dictionary ##pandas #defining_data_frame #csv #dataframe #square #bracket'

loc and iloc - part 2

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out drives_right value of Morocco
print(cars.loc[['MOR', 'drives_right']])

# Print sub-DataFrame
print(cars.iloc[[4,5], [1,2]])

##dictionary ##pandas #defining_data_frame #csv #dataframe #square #bracket'

loc and iloc - part 3

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out drives_right column as Series
print(cars.loc[:, 'drives_right'])
print(cars.iloc[:, 1])

# Print out drives_right column as DataFrame
print(cars.loc[:, ['drives_right']])
print(cars.iloc[:, [1]]) #[] square brackets are very sensitive here


# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:, ['drives_right', 'cars_per_cap']])
print(cars.iloc[:, [0,2]]) #[] square brackets are very sensitive here

#list #forloop #for #loop ##dictionary

Loop over dictionary

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }
          
# Iterate over europe
for key, value in europe.items() :
    print('the capital of ' + str(key) + ' is ' + str(value))

#list #forloop #for #loop ##dictionary

Loop over numpy array

# Import numpy as np

import numpy as np
#for x in my_array : #in 1D Numpy array
#for x in np.nditer(my_array) : #for 2D Numpy array

# For loop over np_height

for x in np_height:
    print(str(x) + " inches")

# For loop over np_baseball
for x in (np.nditer(np_baseball)):
    print(x)

#list #forloop #for #loop ##dictionary

Loop over Dataframe - Pandas

#Iterating over a Pandas DataFrame is typically done with the iterrows()
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Iterate over rows of cars
for lab,row in cars.iterrows():
    print(lab)
    print(row)

#list #forloop #for #loop ##dictionary

Loop over Dataframe - Pandas: Part 2

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Adapt for loop
for lab, row in cars.iterrows() :
    print(lab + ": " + str(row['cars_per_cap']))

#Something new

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows():
    cars.loc[lab,'COUNTRY'] = row['country'].upper()


# Print cars
print(cars)

#list #forloop #for #loop ##dictionary

adding columns to pandas dataframe

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Use .apply(str.upper)


cars['COUNTRY'] = cars['country'].apply(str.upper)
print(cars)

#list #forloop #for #loop ##dictionary

Random Numbers

# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())

#Roll The Dice
# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Use randint() to simulate a dice
print(np.random.randint(1,7))
# Use randint() again
print(np.random.randint(1,7))

loc and iloc in Pandas

Save snippets that work from anywhere online with our extensions

Comments

More like this

Pandas

Browse more snippets >>

loc and iloc in Pandas

Save snippets that work from anywhere online with our extensions

Comments

More like this

Pandas

Browse more snippets >>

Embed code snippet