read csv uisng pandas
import pandas as pd #import pandas
#syntax: pd.read_csv('file_location/file_name.csv')
data = pd.read_csv('filelocation/fileName.csv') #reading data from csvread csv uisng pandas
import pandas as pd #import pandas
#syntax: pd.read_csv('file_location/file_name.csv')
data = pd.read_csv('filelocation/fileName.csv') #reading data from csvcommand 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 querypandas 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
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