[Section2] Pandas Series Fundamentals

해당 내용은 Udemy 강의 'Pandas 및 Python을 이용한 데이터 분석:마스터 클래스'를 수강 후 정리한 내용입니다.

https://www.udemy.com/course/best-pandas-python

Section1

DEFINE A PANDAS SERIES (WITH NUMERIC DEFAULT INDEX)

MINI CHALLENGE #1:

• Define a Pandas Series named "my_series" that contains your top 3 favourite movies. Confirm the datatype of "my_series"

my_list = ['서울의 봄', '친구', '신과함께']
my_series = pd.Series(data = my_list)
my_series

type(my_series)

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Section2

DEFINE A PANDAS SERIES WITH CUSTOM INDEX

MINI CHALLENGE #2:

• Define a Pandas Series named "my_series" that contains your top 3 favourite movies. Instead of using default numeric indexes (similar to mini challenge #1), use the following indexes "movie #1", "Movie #2", and "movie #3"

my_list = ['서울의 봄', '친구', '신과함께']
my_labels = ['movie#1', 'movie#2', 'movie#3']
print(my_labels)

my_series = pd.Series(data = my_list, index = my_labels)
print(my_series)

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Section3

DEFINE A PANDAS SERIES FROM A DICTIONARY

MINI CHALLENGE #3:

• Create a Pandas Series from a dictionary with 3 of your favourite stocks and their corresponding prices

stock_dict = {'GOOG': 193.17,
            'META': 615.86,
            'AMZN': 218.94}

my_stock = pd.Series(data = stock_dict)

print(my_stock)

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Section4

PANDAS ATTRIBUTES

• Attributes/Properties: do not use parantheses "()" and are used to get Pandas Series Properties.
• Methods: use parantheses "()" and might include arguments and they actually alter/change the Pandas Series.
• Indexers: use square brackets "[]" and are used to access specific elements in a Pandas Series or DataFrame.

MINI CHALLENGE #4:

• What is the size of the Pandas Series? (External Research for the proper attribute is Required)

my_series.size

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Section5

PANDAS METHODS

MINI CHALLENGE #5:

• Show the last 2 rows in the Pandas Series (External Research is Required)
• How many bytes does this Pandas Series consume in memory? (External Research is Required)

my_series.tail(2)

my_series.memory_usage()

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Section6

IMPORT CSV DATA (1-D) USING PANDAS

MINI CHALLENGE #6:

• Set Squeeze = False and rerun the cell, what do you notice? Use Type to compare both outputs

squeeze 매개변수는 제거되었기 때문에 MINI CHALLENGE #6는 PASS

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Section7

PANDAS BUILT-IN FUNCTIONS

Built-in Functions

MINI CHALLENGE #7:

• Given the following Pandas Series, convert all positive values to negative using python built-in functions
• Obtain only unique values (ie: Remove duplicates) using python built-in functions
• my_series = pd.Series(data = [-10, 100, -30, 50, 100])

my_series = pd.Series(data = [-10, 100, -30, 50, 100])
my_series

abs(my_series)

set(my_series)

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Section8

SORTING PANDAS SERIES

MINI CHALLENGE #8:

• Sort the S&P500 values in a decending order instead. Make sure to update values in-memory.

sp500 = sp500.sort_values(ascending=False)

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Section9

PERFORM MATH OPERATIONS ON PANDAS SERIES

MINI CHALLENGE #9:

• Obtain the average price of the S&P500 using two different methods

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Section10

CHECK IF A GIVEN ELEMENT EXISTS IN A PANDAS SERIES

# Check if a given number exists in a Pandas Series values
# Returns a boolean "True" or "False"
1295.500000 in sp500.values

# Check if a given number exists in a Pandas Series index
1295.500000 in sp500.index

# Note that by default 'in' will search in Pandas index and not values

MINI CHALLENGE #10:

• Check if the stock price 3349 exists in the sp500 Pandas Series or not
• Round stock prices to the nearest integer and check again

  sp500 = round(sp500)
  
  3349 in sp500.values

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Section11

INDEXING: OBTAIN SPECIFIC ELEMENTS FROM PANDAS SERIES

MINI CHALLENGE #11:

• Obtain the fifth element in the Pandas Series

sp500[4]

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Section12

SLICING: OBTAIN MULTIPLE ELEMENTS FROM PANDAS SERIES

MINI CHALLENGE #12:

• Obtain all elements in Pandas Series except for the last 3 elements

sp500[:-3]