AayushSameerShah

a = [1, 2, 3]
b = [0, 1, 0]  # Arr with Zeros

c = np.divide(a, b, 
              out=np.zeros_like(a), where=b!=0)

'''Simply replace a and b with your variables'''

AayushSameerShah

plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

AayushSameerShah

# Number of elements (total colors to generate in short)
N = 30 
c = ['hsl('+str(h)+',50%'+',50%)' for h in np.linspace(0, 360, N)]

AayushSameerShah

import pandas as pd
import io
import requests
url="https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv"
s=requests.get(url).content
c=pd.read_csv(io.StringIO(s.decode('utf-8')))

AayushSameerShah

# This can be made SUPER easily in tableau
# visit this link
https://www.youtube.com/watch?v=08xh-vvSUqg

AayushSameerShah

# THIS IS AGAIN GONNA BE AMAZING!
'''
Sometimes we have a LOT - means a million lines of data and making the str.get_dummies("|") will
cause the Insufficient Memory Error.

So instead of just splitting them and making zeros and ones... (well that is good if we want to
keep track the word per movies, but if overall - then that is not good practice)
'''

# NEW THING!

AayushSameerShah

# Define a function in which the group will be passed
def get_nlargest(group):
    
    # That group's group will be created (not complex)
    group = group.groupby('key2').value_col.sum()
    return group.nlargest(5)
  
# X Instead of doing this ↓
df.groupby(['key1', 'key2').apply(lamda group: group...)

AayushSameerShah

series = pd.Series([], dtype= int)
for suite in ['H', 'D', 'S', 'C']:
    for value, number in enumerate(['A'] + list(range(1, 11)) + ['J', 'Q', 'K']):
        series = series.add(pd.Series({str(number) + suite: value + 1}), fill_value= 0)
        
 

AayushSameerShah

# Simple and easy
df.gropby(df.index // n_rows).mean()

AayushSameerShah

plt.rcParams['axes.spines.left'] = False
plt.rcParams['axes.spines.right'] = False
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.spines.bottom'] = False