working with csv files

 1. CSV files

        i. reading csv files from local storage

               df=pd.read_csv('healthcare-dataset-stroke-data.csv')

       ii. making your own column instead of pandas default column

              df=pd.read_csv('healthcare-dataset-stroke-data.csv',index_col='id')

      iii. reading file from some link

           import requests

           from io import StringIO

 url = "link name"

headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:66.0) Gecko/20100101 Firefox/66.0"}

req = requests.get(url, headers=headers)

data = StringIO(req.text)

pd.read_csv(data)

   iv.if there is no column name or column name become as a temple becoming first row value

     pd.read_csv('test.csv',header=1)

v.if you want to select only some of the columns during reading of data

   pd.read_csv('aug_train.csv',usecols=['enrollee_id','gender','education_level'])

vi. want to load only few rows

  pd.read_csv('aug_train.csv',nrows=100)

vi. if you see any abnormal emojis datasets then use encoder -encoding encodes to particular language

  pd.read_csv('zomato.csv',encoding='latin-1')

vii. if there is parser error: sometimes more no. of values in columns than expected:skip bad lines

  pd.read_csv('BX-Books.csv', sep=';', encoding="latin-1",error_bad_lines=False)

viii.if you want to change datatype of column during reading csv files

    pd.read_csv('aug_train.csv',dtype={'target':int})

ix.if there is date in your dataset then while reading date will be string : so date should be date instead

   you use following method to make date=date:

    pd.read_csv('IPL Matches 2008-2020.csv',parse_dates=['date_col_name'])

x. some values will be there in your dataset like NaN-,NaN% etc. which problems you the most so in order to convert them into null values i.e actual NaN use the following method:

   pd.read_csv('aug_train.csv',na_values=['Male',])

  here, Male is converted to NaN value