![]() ![]() Print(my_data) Download test-na_values.csv file ⇓ skiprowsĭf=pd.read_csv(path,skiprows=6) convertersīy using converters option we can parse our input data to convert it to a desired dtype using a conversion function. My_data=my_data.isnull() # new column added My_data=pd.read_csv("D: est-na_values.csv", na_values=blank_values) Using the option na_values we can mark data as NaN and indicate them by using isnull() blank_values = Sales=pd.read_csv("sales1.csv",header=None,skiprows=1, usecols=) In above code display only 3 rd and 4 th columns (first column is 0 th column ) To remove the header will use skiprows=1 sales=pd.read_csv("sales1.csv",header=None,skiprows=1) Note that by using header=None we will include header as first row data. ![]() The file have one header row at top but we want to read only data ( not the headers ) or skip the header. In the above code the header row ( first row or 0 th row ) is treated as data ( not as column headers ). Sales=pd.read_csv("sales1.csv",header=None) If we want to treat the first row as data and not as header then here is the code. ![]() The first row or 0th row will be treated as column headers. The first line in our example csv file is the column headers, this is same as header=0. Sales=pd.read_csv("sales1.csv",index_col='sale_id') header Place the sales1.csv file in the same folder and then run the above code.īy default the DataFrame will add one index column, if we don't want it to add and use one of the column as index column then we can add like this. Reading data from CSV file and creating Pandas DataFrame using read_csv() in Python with optionsĭownload CSV file sales1.csv ⇓ import pandas as pd ![]()
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