GeorgeSeif

(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

class_names = ['zero', 'one', 'two', 'three', 'four', 'five',
               'six', 'seven', 'eight', 'nine']

plt.figure(figsize=(10,10))
for i in range(16):
    plt.subplot(4, 4, i+1)
    plt.xticks([])
    plt.yticks([])

GeorgeSeif

import tensorflow as tf
from tensorflow.keras import datasets, layers, models, optimizers
import matplotlib.pyplot as plt

if tf.__version__ < "2.0.0":
  !pip install --upgrade tensorflow_gpu==2.0

GeorgeSeif

import pandas as pd

players_data = {'Player': ['Superman', 'Batman', 'Thanos', 'Batman', 'Thanos',
   'Superman', 'Batman', 'Thanos', 'Black Widow', 'Batman', 'Thanos', 'Superman'],
   'Year': [2000,2000,2000,2001,2001,2002,2002,2002,2003,2004,2004,2005],
   'Points':[23,43,45,65,76,34,23,78,89,76,92,87]}
   
df = pd.DataFrame(players_data)

print(df)

GeorgeSeif

# Horizontal concat
pd.concat([features_1to5_df, features_6to10_df, features_11to15_df], axis=1)

GeorgeSeif

def membership_map(pandas_series, groups_dict):
    groups = {x: k for k, v in groups_dict.items() for x in v}
    mapped_series = pandas_series.map(groups)
    return mapped_series
    
mapped_data = membership_map(foods, groups_dict)
print(list(mapped_data))

GeorgeSeif

import pandas as pd

foods = pd.Series(["Bread", "Rice", "Steak", "Ham", "Chicken",
                       "Apples", "Potatoes", "Mangoes", "Fish",
                       "Bread", "Rice", "Steak", "Ham", "Chicken",
                       "Apples", "Potatoes", "Mangoes", "Fish",
                       "Apples", "Potatoes", "Mangoes", "Fish",
                       "Apples", "Potatoes", "Mangoes", "Fish",
                       "Bread", "Rice", "Steak", "Ham", "Chicken",
                       "Bread", "Rice", "Steak", "Ham", "Chicken",

GeorgeSeif

from itertools import product
import pandas as pd
import numpy as np

col_names = ["Day", "Month", "Year"]

df = pd.DataFrame(list(product([10, 11, 12], [8, 9], [2018, 2019])),
                   columns=col_names)

df['data'] = np.random.randn(len(df))

GeorgeSeif

from itertools import product
import pandas as pd
import numpy as np

col_names = ["Day", "Month", "Year"]

df = pd.DataFrame(list(product([10, 11, 12], [8, 9], [2018, 2019])),
                   columns=col_names)

df['data'] = np.random.randn(len(df))

GeorgeSeif

df = df.stack()

print(df)

"""
0   Player       Superman
    Year             2000
    Points             23
1   Player         Batman
    Year             2000

GeorgeSeif

groups_df = df.groupby('Player')

for player, group in groups_df:
   print("----- {} -----".format(player))
   print(group)
   print("")
   
### This prints out the following
"""
----- Batman -----