read_csv to read data from CSV files

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Pandas

Read and display data from sales1.csv file.

Download CSV file sales1.csv

import pandas as pd 
sales=pd.read_csv("sales1.csv") 
print(sales)

Output is here

	sale_id  c_id  p_id  product  qty store
0        1     2     3  Monitor    2   ABC
1        2     2     4      CPU    1   DEF
2        3     1     3  Monitor    3   ABC

Place the sales1.csv file in the same folder and then run the above code.

Options :

header

The first line in our example csv file is the column headers, this is same as header=0. The first row or 0th row will be treated as column headers.

If we want to treat the first row as data and not as header then here is the code.

sales=pd.read_csv("sales1.csv",header=None)

Output is here

	0     1     2        3    4      5
0  sale_id  c_id  p_id  product  qty  store
1        1     2     3  Monitor    2    ABC
2        2     2     4      CPU    1    DEF
3        3     1     3  Monitor    3    ABC

In the above code the header row ( first row or 0^th row ) is treated as data ( not as column headers ) .

The file have one header row at top but we want to read only data ( not the headers ) or skip the header. Note that by using header=None we will include header as first row data. To remove the header will use skiprows=1

sales=pd.read_csv("sales1.csv",header=None,skiprows=1)

Output

   0  1  2        3  4    5
0  1  2  3  Monitor  2  ABC
1  2  2  4      CPU  1  DEF
2  3  1  3  Monitor  3  ABC

In above code display only 3^rd and 4^th columns (first column is 0^th column )

sales=pd.read_csv("sales1.csv",header=None,skiprows=1, usecols=[3,4])

Output

         3  4
0  Monitor  2
1      CPU  1
2  Monitor  3

na_values

Using the option na_values we can mark data as NaN and indicate them by using isnull()

blank_values = ["n/a", "na", "--"]
my_data=pd.read_csv("D:\\test-na_values.csv",na_values=blank_values)
my_data['status']=my_data['name'].isnull() # new column added 
print(my_data)

Download test-na_values.csv file

All options

pd.read_csv(filepath_or_buffer, 
sep=’, ‘, delimiter=None, header=’infer’, 
names=None, index_col=None, usecols=None, 
squeeze=False, prefix=None, mangle_dupe_cols=True,
 dtype=None, engine=None, converters=None, 
 true_values=None, false_values=None, 
 skipinitialspace=False, skiprows=None,
 nrows=None, na_values=None, keep_default_na=True,
 na_filter=True, verbose=False,
 skip_blank_lines=True, parse_dates=False, 
 infer_datetime_format=False, keep_date_col=False,
 date_parser=None, dayfirst=False, iterator=False,
 chunksize=None, compression=’infer’, thousands=None,
 decimal=b’.’, lineterminator=None, quotechar='”‘, 
 quoting=0, escapechar=None, comment=None, encoding=None,
 dialect=None, tupleize_cols=None, error_bad_lines=True,
 warn_bad_lines=True, skipfooter=0, doublequote=True, 
 delim_whitespace=False, low_memory=True,
 memory_map=False, float_precision=None)

From MySQL to CSV

This is a common requirement as we read data from MySQL database and then save the data in CSV file.
We will further extend this script to read from CSV file and store data in MySQL database.

We are going to use sqlalchemy for our MySQL database connection.

We are first connecting to MySQL database by using our connection userid, password and database name ( db_name ). Then using read_sql() to run the query to get data from student table.

We are writing the data to CSV file by using to_csv().

In the 2nd part of the script we are reading the data from CSV file by using read_csv() and creating a DataFrame. Then we are creating the table by using to_sql(). Here is the complete code.

import pandas as pd 
from sqlalchemy import create_engine
engine = create_engine("mysql+mysqldb://userid:password@localhost/db_name")

sql="SELECT * FROM student "
my_data = pd.read_sql(sql,engine )
my_data.to_csv('D:\my_file.csv',index=False)
### End of storing data to CSV file ###

### Reading data from CSV file and creating table in MySQL ####
student3=pd.read_csv("D:\my_file.csv")
my_data = pd.DataFrame(data=student3)
print(my_data)
### Creating new table student3 or appending existing table 
my_data.to_sql(con=engine,name='student3',if_exists='append')

Data input and output from Pandas DataFrame
Pandas read_excel() to_csv() to_excel()

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