from google.colab import drive
drive.mount('/content/drive')

!pip install kaggle

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))
  
# Then move kaggle.json into the folder where the API expects to find it.
!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json

mypath = '/content/drive/My Drive/Colab Notebooks/kaggle/'



!kaggle competitions list -s sports

!kaggle competitions list -s finance

!kaggle competitions list -s movies

!kaggle competitions list --category research

!kaggle competitions files march-madness-analytics-2020 

!kaggle competitions leaderboard ubiquant-market-prediction -s



!kaggle datasets list -s soccer

!kaggle datasets list 

!kaggle datasets list --sort-by votes

!kaggle datasets files hugomathien/soccer 

!kaggle datasets files hikne707/big-five-european-soccer-leagues  

!kaggle datasets download hikne707/big-five-european-soccer-leagues  



!unzip big-five-european-soccer-leagues.zip

import os
import pandas as pd

df = pd.read_csv('BIG FIVE 1995-2019.csv')

df.shape

df.info()





df





!kaggle datasets files gravix/european-soccer 

!kaggle datasets download gravix/european-soccer 

!unzip european-soccer.zip

df = pd.read_csv('european_soccer.csv')

df

df.info()

df.shape

df.columns

import numpy as np # linear algebra
import os # accessing directory structure
import pandas as pd

nRowsRead = 1000 # specify 'None' if want to read whole file
# european_soccer.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows
df1 = pd.read_csv('european_soccer.csv', delimiter=',', nrows = nRowsRead)
df1.dataframeName = 'european_soccer.csv'
nRow, nCol = df1.shape
print(f'There are {nRow} rows and {nCol} columns')

df1