TransferMarkt Player Recorded Transfer Fees Data Engineering¶

Notebook to engineer player recorded transfer fee data scraped from TransferMarkt.¶

By Edd Webster ¶

Notebook first written: 22/08/2021
Notebook last updated: 22/08/2021

Introduction ¶

This notebook engineers transfer data data from TransferMarkt provided by ewenme, and manipulates this landed data as DataFrames using pandas and matplotlib for visualisation.

For more information about this notebook and the author, I'm available through all the following channels:

title

The accompanying GitHub repository for this notebook can be found here and a static version of this notebook can be found here.

1. Notebook Dependencies ¶

This notebook was written using Python 3 and requires the following libraries:

Jupyter notebooks for this notebook environment with which this project is presented;
NumPy for multidimensional array computing;
pandas for data analysis and manipulation; and
matplotlib for data visualisations.

All packages used for this notebook except for BeautifulSoup can be obtained by downloading and installing the Conda distribution, available on all platforms (Windows, Linux and Mac OSX). Step-by-step guides on how to install Anaconda can be found for Windows here and Mac here, as well as in the Anaconda documentation itself here.

Import Libraries and Modules¶

In [1]:

# Python ≥3.5 (ideally)
import platform
import sys, getopt
assert sys.version_info >= (3, 5)
import csv

# Import Dependencies
%matplotlib inline

# Math Operations
import numpy as np
import math
from math import pi

# Datetime
import datetime
from datetime import date
import time

# Data Preprocessing
import pandas as pd
#import pandas_profiling as pp
import os
import re
import random
from io import BytesIO
from pathlib import Path

# Reading directories
import glob
import os
from os.path import basename

# Flatten lists
from functools import reduce

# Working with JSON
import json
from pandas.io.json import json_normalize

# Web Scraping
import requests
from bs4 import BeautifulSoup
import re

# Currency Convert
from forex_python.converter import CurrencyRates

# APIs
from tyrone_mings import * 

# Fuzzy Matching - Record Linkage
import recordlinkage
import jellyfish
import numexpr as ne

# Data Visualisation
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
import missingno as msno

# Progress Bar
from tqdm import tqdm

# Display in Jupyter
from IPython.display import Image, YouTubeVideo
from IPython.core.display import HTML

# Ignore Warnings
import warnings
warnings.filterwarnings(action="ignore", message="^internal gelsd")

print('Setup Complete')

Setup Complete

In [2]:

# Python / module versions used here for reference
print('Python: {}'.format(platform.python_version()))
print('NumPy: {}'.format(np.__version__))
print('pandas: {}'.format(pd.__version__))
print('matplotlib: {}'.format(mpl.__version__))

Python: 3.7.6
NumPy: 1.20.3
pandas: 1.3.2
matplotlib: 3.4.2

Defined Filepaths¶

In [3]:

# Set up initial paths to subfolders
base_dir = os.path.join('..', '..', )
data_dir = os.path.join(base_dir, 'data')
data_dir_tm = os.path.join(base_dir, 'data', 'tm')
img_dir = os.path.join(base_dir, 'img')
fig_dir = os.path.join(base_dir, 'img', 'fig')
video_dir = os.path.join(base_dir, 'video')

Defined Variables¶

In [5]:

# Define today's date
today = datetime.datetime.now().strftime('%d/%m/%Y').replace('/', '')

Defined Lists¶

In [4]:

# Defined Lists

## Define list of league codes
df_leagues = pd.read_csv(data_dir_tm + '/reference/tm_leagues_comps.csv')
lst_league_codes = df_leagues['league_code'].to_numpy().tolist()

## Define list of 'Big 5' European Leagues and MLS league names
lst_big5_mls_leagues = ['1 Bundesliga', 'Ligue 1', 'Premier League', 'Serie A', 'Primera Division', 'MLS']

## Define list of 'Big 5' European Leagues and MLS codes
lst_big5_mls_league_codes = ['GB1', 'FR1', 'L1', 'IT1', 'ES1', 'MLS1']

Defined Dictionaries¶

In [30]:

### Defined Dictionaries

## League names and TM codes
dict_league_codes = {'Primera Division': 'ES1',
                     'Ligue 1': 'FR1',
                     'Premier League': 'GB1',
                     'Championship': 'GB2',
                     'Serie A': 'IT1',
                     '1 Bundesliga': 'L1',
                     'MLS': 'MLS1',
                     'Eredivisie': 'NL1',
                     'Liga Nos': 'PO1',
                     'Premier Liga': 'RU1'
                    }

Notebook Settings¶

In [6]:

# Display all columns of pandas DataFrames
pd.set_option('display.max_columns', None)

2. Project Brief ¶

This Jupyter notebook is part of a series of notebooks to scrape, parse, engineer, unify, and the model, culminating in a an Expected Transfer (xTransfer) player performance vs. valuation model. This model aims to determine the under- and over-performing players based on their on-the-pitch output against transfer fee and wages.

This particular notebook is one of several data engineering notebooks, that cleans recorded transfer fee player data from TransferMarkt using pandas.

TransferMarkt is a German-based website owned by Axel Springer and is the leading website for the football transfer market. The website posts football related data, including: scores and results, football news, transfer rumours, and most usefully for us - calculated estimates ofthe market values for teams and individual players.

To read more about how these estimations are made, Beyond crowd judgments: Data-driven estimation of market value in association football by Oliver Müllera, Alexander Simons, and Markus Weinmann does an excellent job of explaining how the estimations are made and their level of accuracy.

This notebook, along with the other notebooks in this project workflow are shown in the following diagram:

roadmap

Links to these notebooks in the football_analytics GitHub repository can be found at the following:

3. Data Sources ¶

3.1. Introduction ¶

Before conducting our EDA, the data needs to be imported as a DataFrame in the Data Sources section Section 3 and cleaned in the Data Engineering section Section 4.

We'll be using the pandas library to import our data to this workbook as a DataFrame.

3.2. Data Dictionaries ¶

The TransferMarkt dataset has six features (columns) with the following definitions and data types:

Feature	Data type
`position_number`	object
`position_description`	object
`name`	object
`dob`	object
`nationality`	object
`value`	object

3.3. Read in Data ¶

In [15]:

# Unify individual CSV files as a single DataFrame

## Show files in directory
all_files = glob.glob(os.path.join(data_dir_tm + f'/raw/transfer_history/*/*/*.csv'))

## Create an empty list of Players URLs
lst_player_transfer_history_all = []

## Loop through list of files and read into temporary DataFrames, appending to Player URL list 
for filename in all_files:
    df_temp = pd.read_csv(filename, index_col=None, header=0)
    lst_player_transfer_history_all.append(df_temp)

## Concatenate the files into a single DataFrame
df_tm_transfer_history_all_raw = pd.concat(lst_player_transfer_history_all, axis=0, ignore_index=True)


# Sort DataFrame
df_tm_transfer_history_all_raw = df_tm_transfer_history_all.sort_values(['league_name', 'season', 'player_name'], ascending=[True, True, True])


# Display DataFrame
df_tm_transfer_history_all_raw.head()

In [43]:

# Export DataFrame
    
##
df_tm_transfer_history_all_raw.to_csv(data_dir_tm + f'/raw/transfer_history/tm_player_transfer_history_latest.csv', index=None, header=True)

##
#df_tm_transfer_history_all_raw.to_csv(data_dir_tm + f'/raw/transfer_history/archive/tm_player_transfer_history_last_updated_{today}.csv', index=None, header=True)

In [19]:

df_tm_transfer_history_all_raw.shape

Out[19]:

(169208, 12)

3.4. Preliminary Data Handling ¶

Let's quality of the dataset by looking first and last rows in pandas using the head() and tail() methods.

3.4.1. Summary Report ¶

Initial step of the data handling and Exploratory Data Analysis (EDA) is to create a quick summary report of the dataset using pandas Profiling Report.

In [ ]:

# Summary of the data using pandas Profiling Report
#pp.ProfileReport(df_tm_transfer_history_all_raw)

3.4.1. Further Inspection ¶

The following commands go into more bespoke summary of the dataset. Some of the commands include content covered in the pandas Profiling summary above, but using the standard pandas functions and methods that most peoplem will be more familiar with.

First check the quality of the dataset by looking first and last rows in pandas using the head() and tail() methods.

In [20]:

# Display the first five rows of the raw DataFrame, df_tm_transfer_history_all_raw
df_tm_transfer_history_all_raw.head()

Out[20]:

	club_name	player_name	age	position	club_involved_name	fee	transfer_movement	transfer_period	fee_cleaned	league_name	year	season
159524	VfB Stuttgart	Adrian Knup	23.0	Centre-Forward	FC Luzern	?	in	Summer	NaN	1 Bundesliga	1992	1992/1993
159671	1. FC Köln	Adrian Spyrka	24.0	Central Midfield	Stuttg. Kickers	End of loanJun 30, 1992	in	Summer	0.0	1 Bundesliga	1992	1992/1993
159545	Karlsruher SC	Alexander Famulla	31.0	Goalkeeper	FC 08 Homburg	?	out	Summer	NaN	1 Bundesliga	1992	1992/1993
159510	SV Werder Bremen	Alexander Malchow	22.0	Centre-Back	VfB Oldenburg	Free transfer	out	Summer	0.0	1 Bundesliga	1992	1992/1993
159699	SG Dynamo Dresden	Alexander Zickler	18.0	Centre-Forward	D. Dresden U19	-	in	Summer	0.0	1 Bundesliga	1992	1992/1993

In [21]:

# Display the last five rows of the raw DataFrame, df_tm_transfer_history_all_raw
df_tm_transfer_history_all_raw.tail()

Out[21]:

	club_name	player_name	age	position	club_involved_name	fee	transfer_movement	transfer_period	fee_cleaned	league_name	year	season
69864	FC Crotone	Zak Ruggiero	20.0	Second Striker	Pro Sesto	loan transfer	out	Winter	NaN	Serie A	2020	2020/2021
69523	SSC Napoli	Zinédine Machach	24.0	Central Midfield	VVV-Venlo	loan transfer	out	Summer	NaN	Serie A	2020	2020/2021
69137	Cagliari Calcio	Zito Luvumbo	18.0	Right Winger	1º de Agosto	£900Th.	in	Summer	0.9	Serie A	2020	2020/2021
69762	Torino FC	Álex Berenguer	25.0	Left Winger	Athletic	£10.80m	out	Summer	10.8	Serie A	2020	2020/2021
69423	Juventus FC	Álvaro Morata	27.0	Centre-Forward	Atlético Madrid	Loan fee:£9.00m	in	Summer	9.0	Serie A	2020	2020/2021

In [22]:

# Print the shape of the raw DataFrame, df_tm_transfer_history_all_raw
print(df_tm_transfer_history_all_raw.shape)

(169208, 12)

In [23]:

# Print the column names of the raw DataFrame, df_tm_transfer_history_all_raw
print(df_tm_transfer_history_all_raw.columns)

Index(['club_name', 'player_name', 'age', 'position', 'club_involved_name',
       'fee', 'transfer_movement', 'transfer_period', 'fee_cleaned',
       'league_name', 'year', 'season'],
      dtype='object')

The dataset has twelve features (columns).

In [24]:

# Data types of the features of the raw DataFrame, df_tm_transfer_history_all_raw
df_tm_transfer_history_all_raw.dtypes

Out[24]:

club_name              object
player_name            object
age                   float64
position               object
club_involved_name     object
fee                    object
transfer_movement      object
transfer_period        object
fee_cleaned           float64
league_name            object
year                    int64
season                 object
dtype: object

In [25]:

# Info for the raw DataFrame, df_tm_transfer_history_all_raw
df_tm_transfer_history_all_raw.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 169208 entries, 159524 to 69423
Data columns (total 12 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   club_name           169208 non-null  object 
 1   player_name         169208 non-null  object 
 2   age                 169155 non-null  float64
 3   position            169205 non-null  object 
 4   club_involved_name  169208 non-null  object 
 5   fee                 169064 non-null  object 
 6   transfer_movement   169208 non-null  object 
 7   transfer_period     154776 non-null  object 
 8   fee_cleaned         141044 non-null  float64
 9   league_name         169208 non-null  object 
 10  year                169208 non-null  int64  
 11  season              169208 non-null  object 
dtypes: float64(2), int64(1), object(9)
memory usage: 16.8+ MB

In [26]:

# Description of the raw DataFrame, df_tm_transfer_history_all_raw, showing some summary statistics for each numberical column in the DataFrame
df_tm_transfer_history_all_raw.describe()

Out[26]:

	age	fee_cleaned	year
count	169155.000000	141044.000000	169208.000000
mean	24.364648	0.826293	2008.330008
std	6.266924	3.733992	7.648351
min	-1776.000000	0.000000	1992.000000
25%	21.000000	0.000000	2003.000000
50%	24.000000	0.000000	2009.000000
75%	27.000000	0.000000	2015.000000
max	117.000000	199.800000	2020.000000

In [27]:

# Plot visualisation of the missing values for each feature of the raw DataFrame, df_tm_transfer_history_all_raw
msno.matrix(df_tm_transfer_history_all_raw, figsize = (30, 7))

Out[27]:

<AxesSubplot:>

In [28]:

# Counts of missing values
null_value_stats = df_tm_transfer_history_all_raw.isnull().sum(axis=0)
null_value_stats[null_value_stats != 0]

Out[28]:

age                   53
position               3
fee                  144
transfer_period    14432
fee_cleaned        28164
dtype: int64

The visualisation shows us very quickly that there a few missing values in the age, position, fee, and then more missing values in the transfer_period and fee_cleaned column.

4. Data Engineering ¶

Before we answer the questions in the brief through Exploratory Data Analysis (EDA), we'll first need to clean and wrangle the datasets to a form that meet our needs.

4.1. Assign Raw DataFrames to New Engineered DataFrames ¶

In [31]:

# Assign Raw DataFrames to new Engineered DataFrames
df_tm_transfer_history_all = df_tm_transfer_history_all_raw.copy()

In [34]:

df_tm_transfer_history_all.head()

Out[34]:

	club_name	player_name	age	position	club_involved_name	fee	transfer_movement	transfer_period	fee_cleaned	league_name	year	season	league_code
159524	VfB Stuttgart	Adrian Knup	23.0	Centre-Forward	FC Luzern	?	in	Summer	NaN	1 Bundesliga	1992	1992/1993	L1
159671	1. FC Köln	Adrian Spyrka	24.0	Central Midfield	Stuttg. Kickers	End of loanJun 30, 1992	in	Summer	0.0	1 Bundesliga	1992	1992/1993	L1
159545	Karlsruher SC	Alexander Famulla	31.0	Goalkeeper	FC 08 Homburg	?	out	Summer	NaN	1 Bundesliga	1992	1992/1993	L1
159510	SV Werder Bremen	Alexander Malchow	22.0	Centre-Back	VfB Oldenburg	Free transfer	out	Summer	0.0	1 Bundesliga	1992	1992/1993	L1
159699	SG Dynamo Dresden	Alexander Zickler	18.0	Centre-Forward	D. Dresden U19	-	in	Summer	0.0	1 Bundesliga	1992	1992/1993	L1

4.2. String Cleaning ¶

In [35]:

# Map TM league codes to DataFrame
df_tm_transfer_history_all['league_code'] = df_tm_transfer_history_all['league_name'].map(dict_league_codes)

4.3. Convert to GBP (£)¶

In [36]:

# Currency Convert
from currency_converter import CurrencyConverter
import math

In [37]:

# Get EUR to GBP exchange rate

## Get latest currency rates
c = CurrencyConverter()

##  Get conversion rate from EUR to GBP
rate_eur_gbp = (c.convert(1, 'EUR', 'GBP'))
rate_eur_gbp

Out[37]:

0.90053

In [38]:

# Overwrite
rate_eur_gbp = 0.90
rate_eur_gbp

Out[38]:

0.9

In [39]:

# NOT NEEDED AS ALREADY IN POUNDS

# Convert Euros to Pounds
#df_tm_transfer_history_all['fee_cleaned_pounds'] = df_tm_transfer_history_all['fee_cleaned'] * rate_eur_gbp

4.4. Filter Players in 'Big 5' European Leagues and MLS ¶

No MLS data in the dataset currently

In [44]:

df_tm_transfer_history_all['league_name'].unique()

Out[44]:

array(['1 Bundesliga', 'Championship', 'Eredivisie', 'Liga Nos',
       'Ligue 1', 'Premier League', 'Premier Liga', 'Primera Division',
       'Serie A'], dtype=object)

In [46]:

# Filter list of 'Big 5' European Leagues and MLS from DataFrame
df_tm_transfer_history_big5 = df_tm_transfer_history_all[df_tm_transfer_history_all['league_name'].isin(lst_big5_mls_leagues)]

In [49]:

df_tm_transfer_history_big5

Out[49]:

	club_name	player_name	age	position	club_involved_name	fee	transfer_movement	transfer_period	fee_cleaned	league_name	year	season	league_code
159524	VfB Stuttgart	Adrian Knup	23.0	Centre-Forward	FC Luzern	?	in	Summer	NaN	1 Bundesliga	1992	1992/1993	L1
159671	1. FC Köln	Adrian Spyrka	24.0	Central Midfield	Stuttg. Kickers	End of loanJun 30, 1992	in	Summer	0.0	1 Bundesliga	1992	1992/1993	L1
159545	Karlsruher SC	Alexander Famulla	31.0	Goalkeeper	FC 08 Homburg	?	out	Summer	NaN	1 Bundesliga	1992	1992/1993	L1
159510	SV Werder Bremen	Alexander Malchow	22.0	Centre-Back	VfB Oldenburg	Free transfer	out	Summer	0.0	1 Bundesliga	1992	1992/1993	L1
159699	SG Dynamo Dresden	Alexander Zickler	18.0	Centre-Forward	D. Dresden U19	-	in	Summer	0.0	1 Bundesliga	1992	1992/1993	L1
...	...	...	...	...	...	...	...	...	...	...	...	...	...
69864	FC Crotone	Zak Ruggiero	20.0	Second Striker	Pro Sesto	loan transfer	out	Winter	NaN	Serie A	2020	2020/2021	IT1
69523	SSC Napoli	Zinédine Machach	24.0	Central Midfield	VVV-Venlo	loan transfer	out	Summer	NaN	Serie A	2020	2020/2021	IT1
69137	Cagliari Calcio	Zito Luvumbo	18.0	Right Winger	1º de Agosto	£900Th.	in	Summer	0.9	Serie A	2020	2020/2021	IT1
69762	Torino FC	Álex Berenguer	25.0	Left Winger	Athletic	£10.80m	out	Summer	10.8	Serie A	2020	2020/2021	IT1
69423	Juventus FC	Álvaro Morata	27.0	Centre-Forward	Atlético Madrid	Loan fee:£9.00m	in	Summer	9.0	Serie A	2020	2020/2021	IT1

93489 rows × 13 columns

In [48]:

df_tm_transfer_history_big5.shape

Out[48]:

(93489, 13)

4.5. Filter for 2016/2017 Season Onwards ¶

Separate DataFrame created for transfers from 2016/2017 onwards as this is when the other data sources such as FBref start.

In [52]:

# Filter plays in the Big 5 European Leagues

## Define list of countries
lst_seasons_1617_2122 = ['2016/2017', '2017/2018', '2018/2019', '2019/2020', '2020/2021', '2021/2022']

## Filter list of Big 5 European League countries from DataFrame
df_tm_transfer_history_big5_1617_present = df_tm_transfer_history_big5[df_tm_transfer_history_big5['season'].isin(lst_seasons_1617_2122)]

In [54]:

df_tm_transfer_history_big5_1617_present.head()

Out[54]:

	club_name	player_name	age	position	club_involved_name	fee	transfer_movement	transfer_period	fee_cleaned	league_name	year	season	league_code
166018	1.FSV Mainz 05	Aaron Seydel	20.0	Left Winger	FSV Mainz 05 II	-	in	Winter	0.00	1 Bundesliga	2016	2016/2017	L1
165635	FC Schalke 04	Abdul Rahman Baba	22.0	Left-Back	Chelsea	Loan fee:£450Th.	in	Summer	0.45	1 Bundesliga	2016	2016/2017	L1
165998	Borussia Dortmund	Adrián Ramos	31.0	Centre-Forward	Granada CF	Loan	out	Winter	0.00	1 Bundesliga	2016	2016/2017	L1
165809	FC Augsburg	Albian Ajeti	19.0	Centre-Forward	FC St. Gallen	Loan	out	Summer	0.00	1 Bundesliga	2016	2016/2017	L1
165833	SV Werder Bremen	Alejandro Gálvez	27.0	Centre-Back	SD Eibar	£1.35m	out	Summer	1.35	1 Bundesliga	2016	2016/2017	L1

In [55]:

df_tm_transfer_history_big5_1617_present.shape

Out[55]:

(19756, 13)

5. Exporting the Engineered DataFrames ¶

Export the two engineered TransferMarkt DataFrames as CSV files.

In [42]:

# Export DataFrame
    
##
df_tm_transfer_history_all.to_csv(data_dir_tm + f'/engineered/transfer_history/tm_player_transfer_history_latest.csv', index=None, header=True)

##
df_tm_transfer_history_all.to_csv(data_dir_tm + f'/engineered/transfer_history/archive/tm_player_transfer_history_last_updated_{today}.csv', index=None, header=True)

In [ ]:

# FINISH OFF THIS SECTION

In [ ]:

## Filename
file_name = 'tm_player_joined_big5_mls'

## Full season string
full_season_string = '2016/2017 - 2021/2022'

## Short season string
short_season_string = '1617-2122'

In [ ]:

# Export DataFrame as a CSV file

## Export a copy to the 'archive' subfolder of the TM folder, including the date
#df_tm_market_value_bio_status_select.to_csv(data_dir_tm + f'/engineered/{short_season_string}/joined/archive/' + f'{file_name}_{short_season_string}_last_updated_{today}.csv', index=None, header=True)

## Export another copy to the TM folder called 'latest' (can be overwritten)
#df_tm_market_value_bio_status_select.to_csv(data_dir_tm + f'/engineered/{short_season_string}/joined/' + f'{file_name}_{short_season_string}_latest.csv', index=None, header=True)

In [ ]:

# Export DataFrame as a CSV file

## All data
#df_tm_transfer_history_all.to_csv(data_dir_tm + f'/engineered/transfers/archive/' + f'tm_player_transfers_all_last_updated_{today}.csv', index=None, header=True)
#df_tm_transfer_history_all.to_csv(data_dir_tm + f'/engineered/transfers/' + f'tm_transfers_player_all_latest.csv', index=None, header=True)
#df_tm_transfer_history_all.to_csv(data_dir + f'/export/' + f'tm_transfers_player_all_latest.csv', index=None, header=True)


## Big 5 European Leagues
#df_transfers_big5.to_csv(data_dir_tm + f'/engineered/transfers/archive/' + f'tm_player_transfers_big5_last_updated_{today}.csv', index=None, header=True)
#df_transfers_big5.to_csv(data_dir_tm + f'/engineered/transfers/' + f'tm_player_transfers_big5_latest.csv', index=None, header=True)
#df_transfers_big5.to_csv(data_dir + f'/export/' + f'tm_player_transfers_big5_latest.csv', index=None, header=True)

Data and Web Scraping¶

Beyond crowd judgments: Data-driven estimation of market value in association football by Oliver Müllera, Alexander Simons, and Markus Weinmann.
06/04/2020: BBC - Premier League squads 'drop £1.6bn in value'.

*Visit my website eddwebster.com or my GitHub Repository for more projects. If you'd like to get in contact, my Twitter handle is @eddwebster and my email is: edd.j.webster@gmail.com.*

Back to the top

TransferMarkt Player Recorded Transfer Fees Data Engineering¶

Notebook to engineer player recorded transfer fee data scraped from TransferMarkt.¶

By Edd Webster ¶

Introduction ¶

Notebook Contents ¶

1. Notebook Dependencies ¶

Import Libraries and Modules¶

Defined Filepaths¶

Defined Variables¶

Defined Lists¶

Defined Dictionaries¶

Notebook Settings¶

2. Project Brief ¶

3. Data Sources ¶

3.1. Introduction ¶

3.2. Data Dictionaries ¶

3.3. Read in Data ¶

3.4. Preliminary Data Handling ¶

3.4.1. Summary Report ¶

3.4.1. Further Inspection ¶

4. Data Engineering ¶

4.1. Assign Raw DataFrames to New Engineered DataFrames ¶

4.2. String Cleaning ¶

4.3. Convert to GBP (£)¶

4.4. Filter Players in 'Big 5' European Leagues and MLS ¶

4.5. Filter for 2016/2017 Season Onwards ¶

5. Exporting the Engineered DataFrames ¶

6. Summary ¶

7. Next Steps ¶

8. References ¶

Data and Web Scraping¶

TransferMarkt Player Recorded Transfer Fees Data Engineering¶

Notebook to engineer player recorded transfer fee data scraped from TransferMarkt.¶

By Edd Webster¶

Import Libraries and Modules¶

Defined Filepaths¶

Defined Variables¶

Defined Lists¶

Defined Dictionaries¶

Notebook Settings¶

Data and Web Scraping¶

By Edd Webster ¶