MY BABY!!! sandy!!! frolicking in the sun!!!!


///////////////   ANSWERS TO  SOME QUESTIONS IN PYTHON: /////////

1. WHAT IS A SERIES AND HOW TO CREATE A SERIES

Series is a one-dimensional labeled array capable of holding 

data of any type (integer, string, float, python objects, etc.)

Heres is the syntax :

>>> pd.Series([1, 2, "Mary", "George"], name="name of series",

index, dtype, copy])



2. WHAT IS A LIST IN PYTHON ###############################

A list is a data structure in Python that is a mutable, or 

changeable, ordered sequence of elements. Each element or 

value that is inside of a list is called an item. Just as 

strings are defined as characters between quotes, lists are 

defined by having values between square brackets [ ]


5. WHAT IS A DICTIONARY IN PYTHON AND WHAT IS THE SYNTAX

A dictionary is a collection which is ordered*, changeable 

and does not allow duplicates. As of Python version 3.7, 

dictionaries are ordered. 


In Python 3.6 and earlier, dictionaries are unordered. 

Dictionaries are written with curly brackets, and have keys 

and values: Example. Create and print a dictionary:

Basic syntax is:

>>> dict = {

  "brand": "Ford",

  "model": "Mustang",

  "year": 1964

}


*** you can also create a key with multiple values (like a 

column with multiple cells...)


dict = {

  "brand": ["Ford", "Chrysler", "Toyota"],

  "model": ["Mustang", "Van", "Camry"],

  "year": [1964, 1971, 1998],

}


6. HOW TO CREATE A SERIES FROM A DICTIONARY?

With PANDAS library you can do this:


df = pd.Series(dict)


7. HOW TO CREATE A DICTIONARY FROM A SERIES?

df = pd.Series.to_dict(dict)




8. PARAMETERS AVAILABLE IN  PANDA'S READ_EXCEL ARE?

pandas.read_excel(

io, 

sheet_name=0, 

header=0, 

names=None, 

index_col=None, 

usecols=None, 

squeeze=False, 

dtype=None, 

engine=None, 

converters=None, 

true_values=None, 

false_values=None, 

skiprows=None, 

nrows=None, 

na_values=None, 

keep_default_na=True, 

na_filter=True, 

verbose=False, 

parse_dates=False, 

date_parser=None, 

thousands=None, 

comment=None, 

skipfooter=0, 

convert_float=True, 

mangle_dupe_cols=True, 

storage_options=None

)

9. Extracting multiple rows with same index

In this example, Team name is made as the index column and one team 

name is passed to .loc method to check if all values with same team 

name have been returned or not.

# importing pandas package 

import pandas as pd 


# making data frame from csv file 

data = pd.read_csv("nba.csv", index_col ="Team") 


# retrieving rows by loc method 

rows = data.loc["Utah Jazz"] 


# checking data type of rows 

print(type(rows)) 


# display 

rows


10. PANDAS CSV READ METHOD

pandas.read_csv(

filepath_or_buffer, 

sep=<object object>, 

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, 

skipfooter=0, 

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, 

cache_dates=True, 

iterator=False, 

chunksize=None, 

compression='infer', 

thousands=None, decimal='.', 

lineterminator=None, 

quotechar='"', 

quoting=0, 

doublequote=True, 

escapechar=None, 

comment=None, 

encoding=None, 

dialect=None, 

error_bad_lines=True, 

warn_bad_lines=True, 

delim_whitespace=False, 

low_memory=True, 

memory_map=False, 

float_precision=None, 

storage_options=None

)



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