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 csv
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 csv
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
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 )
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