AayushSameerShah

import re
def remove(str_):
  return re.sub(pattern, '', str_)

df.column.apply(remove)

AayushSameerShah

# For Vertical
plt.vlines(x= df.index, ymin= df.mean(), ymax= df.values, alpha=0.4, linewidth=5)

# For Horizontal
plt.hlines(y= df.index, xmin= df.mean(), xmax= df.values, alpha=0.4, linewidth=5)

AayushSameerShah

plt.savefig('myfile.png', bbox_inches="tight")

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# Examples can be found in Pandas Book (Notebooks) 3 Section > 2. Combining, Merging 

# This is very manual (but a kind of hack)
df.stack(level= [0, 1]).unstack(level= [1, 2, 3])
         # Desired level and their order ↑ (Both stack / unstack required)
  
 
# Real Way - efficient
df.unstack().swaplevel(2, 0, axis= 1).sort_index(level= 0, aixs= 1)
# This is the  ↑  person.               ↑  This sorting is optional but required for cleaner dataset

AayushSameerShah

'''There can be a situation while cleaning the data
you might want ot replace one dataframe's value on 
certain indices with other df's values (usually cl-
eared ones) based on the index - This MIGHT help.

2 Solutions.'''

# 1. Great - Almost all time will work
df.col_to_change.update(df.col_new_value)
' As both are Series - this will work!  '

AayushSameerShah

# AMAZING!
df.columns.get_level_values(0)

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'''Example data

    Name       Genre
0   TENET      Action|Thriller
1   MEMENTO    Crime|Thriller|Action
2   AVENGERS   Children's
'''
# SPOILER ALERT: This method is the UNDERLYING method. Just use - df.Genre.str.get_dummies("|") for the same result (more on this later)

# Step 1: Get the unique genre

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# Wow way
df[(np.abs(df) > 2).any(1)].style.applymap(lambda x: "background: yellow" if np.abs(x) > 2 else "")
                                 # ↑ Here applymap is used which
                                 #   is eqivalent to the code below
        
# Verbose, but still more control way
df[(np.abs(df) > 2).any(1)].style.apply(lambda x: ["background: yellow" if np.abs(value) > 2 else "" for value in x])
                                # ↑ Only apply is used, so needed to itereate over all elements in this ↑ list comp way
    

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import numpy as np
import matplotlib.pyplot as plt

plt.xkcd() # <-- This does the job
plt.figure()
plt.plot(np.linspace(0.7,1.42,100),[0.7]*100)
plt.show()

AayushSameerShah

DF.dateCol.apply(lambda x: x.replace(year= x.year + 2))