import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib .pyplot as plt
import seaborn as sns
import plotly .graph_objects as go
import plotly .express as px
import chart_studio .plotly as py
import cufflinks as cf
import dash_core_components as dcc
import dash_html_components as html
import country_converter as coco
import geopy
from jupyter_dash import JupyterDash
from dash .dependencies import Input , Output
from plotly .offline import init_notebook_mode , download_plotlyjs , plot , iplot
from geopy .geocoders import Nominatim
from geopy .extra .rate_limiter import RateLimiter pd .set_option ("display.max_rows" , 85 )
pd .set_option ("display.max_columns" , 85 )init_notebook_mode (connected = True )
cf .go_offline ()import functools
def decorator (func ):
@functools .wraps (func )
def wrapper_decorator (* args , ** kwargs ):
# Do something before
value = func (* args , ** kwargs )
# Do something after
return value
return wrapper_decorator def os_check ():
from sys import platform
if platform == "darwin" :
print ("macOS" )
elif platform == "win32" :
print ("Windows" )
elif platform == "linux" or platform == "linux2" :
print ("Linux" )
else :
print ("Oops! Something went wrong." )Check if path is a file or directory def path_checker (path ):
from pathlib import Path
path = Path (path )
if path .exists ():
if path .is_dir ():
print (f"'{ path } )
else :
if path .is_file ():
print (f"'{ path } )
return True
else :
print (f"'{ path } )
return False Unpack list and return with Oxford comma def unpack_list (lst ): # Oxford comma
if not isinstance (lst , str ):
lst = [str (item ) for item in lst ]
if len (lst ) == 0 :
return
if len (lst ) == 1 :
return ", " .join (lst )
if len (lst ) == 2 :
return ", and " .join (lst )
else :
first_part = lst [:- 1 ]
last_part = lst [- 1 ]
return ", " .join (first_part ) + ", and " + last_part Combine two lists to a dictionary months = [1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]
months_alpha = ['Jan' ,'Feb' ,'Mar' ,'Apr' ,'May' ,'Jun' ,'Jul' ,'Aug' ,'Sep' ,'Oct' ,'Nov' ,'Des' ]
dictionary = [{'label' : x ,'value' : y } for x , y in zip (months , months_alpha )]Word search in a DataFrame def word_search (df , * words ):
if not words or len (words [0 ]) < 1 :
return
col_count : int = 0
sum_words : int = 0
found_words : str = []
if isinstance (words [0 ], str ):
words = [word for word in words ]
else :
words = list (* words )
print (words )
for word in words :
col_count = 0
sum_word = 0
for column in df :
if df [column ].dtype == object or df [column ].dtype == str :
col_count += 1
sum_word += df [column ].str .contains (f"^{ word } ).sum ()
if df [column ].str .contains (f"^{ word } ).any ():
if word not in found_words :
found_words .append (word )
sum_words += sum_word
if len (found_words ) == 0 :
found_words = words
print ("Columns of dtype str or object:" , col_count )
print (
f"Instances of { unpack_list (found_words )} { sum_words }
)Find missing values in a DataFrame def find_missing_values (df ):
column_names = (df .columns [df .isnull ().any () == True ]).format ()
miss_columns = df .isna ().any ().sum ()
miss_values = df .isna ().sum ().sum ()
print (f"Instances of missing data: { miss_values } )
print (f"Columns with missing data: { miss_columns } )
print (f"Column names with missing data: { unpack_list (column_names )} )Find location of missing values in a DataFrame def missing_location (df ):
col_criteria = df .isnull ().any (axis = 0 )
miss_col = df [col_criteria .index [col_criteria ]]
miss_only = miss_col [miss_col .isnull ().any (axis = 1 )]
row_criteria = df .isnull ().any (axis = 1 )
miss_row = df [row_criteria ]
return miss_col , miss_row , miss_only Find location data from a DataFrame column def df_locatinon_data (df , search_col ):
from geopy .geocoders import Nominatim
from geopy .extra .rate_limiter import RateLimiter
geolocator = Nominatim (user_agent = "my_geocoder" )
geocode = RateLimiter (geolocator .geocode , min_delay_seconds = .1 )
# Find the location.
df ['location' ] = df [search_col ].apply (geocode )
# Extract point to its own columns.
df ['point' ] = df ['location' ].apply (lambda loc : tuple (loc .point )
if loc else None )
# split point column into latitude, longitude and altitude columns.
df [['latitude' , 'longitude' ,
'altitude' ]] = pd .DataFrame (df ['point' ].tolist (), index = df .index )
return df Change name of index or row items, or to datetime. def replace_df_ax_name (df , find , replace_with = "" , axis = 0 ):
dff = df .copy ()
if axis == 1 : # <-- Columns
dff = dff .T
dff_row = dff .index .to_list ()
dff_dict = {i : dff_row [i ] for i in range (len (dff_row ))}
change_index : list = []
change_dict : dict = {}
for i , v in dff_dict .items ():
if find in v :
change_index .append (i )
if replace_with == "d_to_datetime" :
v = pd .to_datetime (v )
else :
v = v .replace (find , replace_with )
change_dict [i ] = v
dff_dict .update (change_dict )
dff .index = list (dff_dict .values ())
if axis == 1 : # <-- Columns
dff = dff .T
return dff Real Python
squares = [i * i for i in range (10 )]sentence = 'the rocket came back from mars'
vowels = [i for i in sentence if i in 'aeiou' ]
def is_consonant (letter ):
vowels = 'aeiou'
return letter .isalpha () and letter .lower () not in vowels
consonants = [i for i in sentence if is_consonant (i )]original_prices = [1.25 , - 9.45 , 10.22 , 3.78 , - 5.92 , 1.16 ]
prices = [i if i > 0 else 0 for i in original_prices ]quote = "life, uh, finds a way"
unique_vowels = {i for i in quote if i in 'aeiou' }squares = {i : i * i for i in range (10 )}
squares
