pandas csv python code example

Example 1: how to import csv in pandas

import pandas as pd

df = pd.read_csv (r'Path where the CSV file is stored\File name.csv')
print (df)

Example 2: command to read file in python using pandas

import panda as pd 

file_csv = pd.read_csv("file path")  ## as csv format
file_excel = pd.read_excel("file path") ## as excel format
file_json = pd.read_json("file path") ## as json format
file_html = pd.read_html("file path") ## as html format
file_localClipboard = pd.read_clipboard("file path") ## as clipboard format
file_MSExcel = pd.read_excel("file path") ## as excel format
file_HDF5 = pd.read_hdf("file path") ## as hdf5 fomrmat
file_Feather = pd.read_feather("file path") ## as feather format
file_msgpack = pd.read_msgpack("file path") ## as msgpack format
file_stata = pd.read_stata("file path") ## as stata format
file_SAS = pd.read_sas("file path") ## as SAS format
file_paythonPickle = pd.read_pickle("file path") ## as paython_pickle format
file_SQL = pd.read_sql("file path") ## as sql format
file_google_big_query = pd.read_gbq("file path") ## as google big query

Example 3: pandas read csv

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

Example 4: python csv

>>> import csv
>>> with open('names.csv', newline='') as csvfile:
...     reader = csv.DictReader(csvfile)
...     for row in reader:
...         print(row['first_name'], row['last_name'])
...
Eric Idle
John Cleese

>>> print(row)
{'first_name': 'John', 'last_name': 'Cleese'}

Example 5: pandas read csv

import pandas as pd
cereal_df = pd.read_csv("/tmp/tmp07wuam09/data/cereal.csv")
cereal_df2 = pd.read_csv("data/cereal.csv")

# Are they the same?
print(pd.DataFrame.equals(cereal_df, cereal_df2))

Example 6: pandas go through csv file

data = pd.read_csv(
    "data/files/complex_data_example.tsv",      # relative python path to subdirectory
    sep='\t'           # Tab-separated value file.
    quotechar="'",        # single quote allowed as quote character
    dtype={"salary": int},             # Parse the salary column as an integer 
    usecols=['name', 'birth_date', 'salary'].   # Only load the three columns specified.
    parse_dates=['birth_date'],     # Intepret the birth_date column as a date
    skiprows=10,         # Skip the first 10 rows of the file
    na_values=['.', '??']       # Take any '.' or '??' values as NA
)