Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
import pandas as pd
Code #1 : read_csv is an important pandas function to read csv files and do operations on it.
Opening a CSV file through this is easy. But there are many others thing one can do through this function only to change the returned object completely. For instance, one can read a csv file not only locally, but from a URL through read_csv or one can choose what columns needed to export so that we don’t have to edit the array later.
Here is the list of parameters it takes with their Default values.
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)
Not all of them are much important but remembering these actually save time of performing same functions on own. One can see parameters of any function by pressing shift + tab in jupyter notebook. Useful ones are given below with their usage :
|filepath_or_buffer||URL or Dir location of file|
|sep||Stands for seperator, default is ‘, ‘ as in csv(comma seperated values)|
|index_col||Makes passed column as index instead of 0, 1, 2, 3…r|
|header||Makes passed row/s[int/int list] as header|
|use_cols||Only uses the passed col[string list] to make data frame|
|squeeze||If true and only one column is passed, returns pandas series|
|skiprows||Skips passed rows in new data frame|
Refer the link to data set used from here.
Code #2 :
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