Square Bracket in Pandas

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Thu Nov 25 2021 06:44:23 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 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']])

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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

##pandas ##python

find the percentage of missing values in pandas

percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
                                 'percent_missing': percent_missing})

#python #pandas #dataframe

brondata naar pandas dataframe

def load_table_to_df(fp, **kwargs):

    if fp.endswith('.xls'):
        result_df = pd.read_excel(fp, **kwargs)
        
    elif fp.endswith('.xlsx'):
        result_df = pd.read_excel(fp, engine='openpyxl', **kwargs)
        
    elif fp.endswith('.csv') or fp.endswith('.txt'):
        result_df = pd.read_csv(fp, **kwargs)
        
    elif fp.endswith('.parquet'):
        result_df = pd.read_parquet(fp, **kwargs)
        
    else:
        return "Wrong file extension"

    print('     -->  Succesfully loaded {}'.format(fp))
    return result_df

#python #pandas #dataframe

Adding new column to a dataframe

lins = pd.Series(lines, name='img_path')
train_df = pd.concat([train_df, lins], axis=1)

##pandas ##plot

plot aggregate pandas data

def f(x):
    d={}
    d['conversion_rate'] = x.ORDER_PLACED.mean()
    d['n_orders'] = len(x)
    d['aov'] = x.TOTAL_PRICE_PTC.mean()
    return pd.Series(d, index=['conversion_rate','n_orders','aov'])

AGG_CITIES = {
    city: (df
           .query('CITY_PTC==@city')
           .groupby(['COUNTRY_CODE_PTC','CITY_PTC','FREIGHT_PERC_BIN'])
           .apply(f)
           .reset_index())
    for city in df.CITY_PTC.unique()
}

colors = plt.rcParams["axes.prop_cycle"]()
fig, axes = plt.subplots(ncols=2, nrows=2, sharex=True, sharey=True, figsize=(18, 12))

for ax, (city, vals) in zip(axes.ravel(), AGG_CITIES.items()):
    c = next(colors)["color"]
    ax.plot(vals.FREIGHT_PERC_BIN, vals.conversion_rate, color=c, alpha=0.3, label=city)
    ax.set_ylabel('CONVERSION RATE')
    ax.set_xlabel('FREIGHT_PERC_BIN')   
    ax.xaxis.set_tick_params(rotation=65)
#    ax.axvline(x=np.mean(vals), color=c, linestyle="--")
#    ax.axvline(x=0, color="black")
#    ax.set_xlim(-0.3, 0.3)
    ax.legend()

##pandas

pandas - Apply multiple functions to multiple groupby columns

def f(x):
    d = {}
    d['a_sum'] = x['a'].sum()
    d['a_max'] = x['a'].max()
    d['b_mean'] = x['b'].mean()
    d['c_d_prodsum'] = (x['c'] * x['d']).sum()
    return pd.Series(d, index=['a_sum', 'a_max', 'b_mean', 'c_d_prodsum'])

df.groupby('group').apply(f)

#list #forloop #for #loop ##dictionary

Dice Random no

# Numpy is imported, seed is set

# Starting step
step = 50
# Roll the dice
dice = np.random.randint(1,7)
# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice <= 5 :
    step = step + 1
else:
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)

#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))

#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

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

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 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 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))

##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

##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 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]])

Square Bracket in Pandas

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