This code cleans up a big dataset into a very clean and a flat file that can be further used downstream like visualization.
I am expecting to improve the code to essentially run it faster and cleanup the code to avoid any inefficient functions that should not take further resources than it should.
The problem is that I have written the code as and when I have experienced any functions as needed to be added on the fly and now, I am not very happy with it.
I am seeking help from this community on the best code that can be written which performs the same work that this code does. I would also be learning on the best practices while working with pandas.
There is so much on the internet on Pandas that every function and code seems to be a clean code but when i look at it collectively, its so bad.
from datetime import datetime
import pandas as pd
import numpy as np
from tabulate import tabulate
start = datetime.now()
df = pd.read_csv()
#This part essentially splits columns and harmonises the entie dataframe
# This code harmonises the game column e.g. "Talleres (R.E) - Defensores Unidos" should be split as "Talleres - "Defensores Unidos" and removes any date values in the column
df['game'] = df['game'].astype(str).str.replace('(\(\w+\))', '', regex=True)
df['league']df['game'] = df['league']df['game'].astype(str).str.replace('(\s\d+\S\d+)$', '', regex=True)
df['game']# This code removes any numerical values in the league column. Many times the league column has years concatenated which is what we don't want e.g "Brazil Copa do Nordeste 2020" should be "Brazil Copa do Nordeste"
df['league'] = df['game']df['league'].astype(str).str.replace('(\s\d+\S\d+)$', '', regex=True)
# This part splits the game column into tow competing teams i.e. home team and away team by the delimiter "-"
df[['home_team', 'away_team']] = df['game'].str.split(' - ', expand=True, n=1)
# This part splits the game column into tow competing teams i.e. home team and away team by the delimiter ":"
df[['home_score', 'away_score']] = df['score'].str.split(':', expand=True)
# This code removes any non numerical values in the home score and away score columns. e.ge scores can have "aet", "canc", ".", etc. We dont want anything that cannot be identified as filetype:int in pandas
df['away_score'] = df['away_score'].astype(str).str.replace('[a-zA-Z\s\D]', '', regex=True)
df['home_score'] = df['home_score'].astype(str).str.replace('[a-zA-Z\s\D]', '', regex=True)
df = df[df.home_score != "."]
df = df[df.home_score != ".."]
df = df[df.home_score != "."]
df = df[df.home_odds != "-"]
df = df[df.draw_odds != "-"]
df = df[df.away_odds != "-"]
m = df[['home_odds', 'draw_odds', 'away_odds']].astype(str).agg(lambda x: x.str.count('/'), 1).ne(0).all(1)
n = df[['home_score']].agg(lambda x: x.str.count('-'), 1).ne(0).all(1)
o = df[['away_score']].agg(lambda x: x.str.count('-'), 1).ne(0).all(1)
df = df[~m]
df = df[~n]
df = df[~o]
df = df[df.home_score != '']
df = df[df.away_score != '']
df = df.dropna()
# We now would be keeping only the columns we want
df = df.loc[:, df.columns.intersection(
['datetime', 'country', 'league', 'home_team', 'away_team', 'home_odds', 'draw_odds', 'away_odds', 'home_score',
'away_score'])]
#We are making sure that the columns are as per data types that we would want pandas to identify. Pandas does not seem to do a very good job identifying data types correctly.
colt = {
'country': str,
'league': str,
'home_team': str,
'away_team': str,
'home_odds': float,
'draw_odds': float,
'away_odds': float,
'home_score': int,
'away_score': int
}
df = df.astype(colt)
# This part removes any leading and trailing whitespaces in the string columns
df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
# Cleaning data where odds are greater than 100 and less than -1 and dropping duplicates
df = df[df['home_odds'] <= 100]
df = df[df['draw_odds'] <= 100]
df = df[df['away_odds'] <= 100]
df = df.drop_duplicates(['datetime', 'home_score', 'away_score', 'country', 'league', 'home_team', 'away_team'],
keep='last')
df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
df.to_csv()
time_taken = end - start
print('Time taken to complete: ', time_taken)
df.head()
home_odds draw_odds away_odds country league datetime home_team away_team home_score away_score
-- ----------- ----------- ----------- --------- ---------------- ------------------- --------------- ----------- ------------ ------------
0 1.36 4.31 7.66 Brazil Copa do Nordeste 2020-02-07 00:00:00 Sport Recife Imperatriz 2 2
1 2.62 3.3 2.48 Brazil Copa do Nordeste 2020-02-02 22:00:00 ABC America RN 2 1
2 5.19 3.58 1.62 Brazil Copa do Nordeste 2020-02-02 00:00:00 Frei Paulistano Nautico 0 2
3 2.06 3.16 3.5 Brazil Copa do Nordeste 2020-02-02 22:00:00 Botafogo PB Confianca 1 1
4 2.19 2.98 3.38 Brazil Copa do Nordeste 2020-02-02 22:00:00 Fortaleza Ceara 1 1