Overview: You will explore Pandas series and data frame containers and the functions and methods available for these Python containers. You will then use these methods and functions to analyze a CSV data set.

Instructions:

This lab requires two additional files to be downloaded: Pokemon.csv

Part 1 - Observe and Practice (40 points) Through Anaconda Navigator, open Jupyter Notebook. Copy each group of code snippets below into a cell on Jupyand run the code. Copy the output after the code and explain what the code is doing.

a) Series

import pandas as pd series1 = pd.Series([1, 2, 3, 4]) series1

type(series1)

series2 = pd.Series([1, 2, 3, 4], index = ['a','b','c','d']) series2

series3 = series2[1:3] series3

b) Data Frames

list_of_fruit = [['apple', 10], ['mango', 12], ['banana', 13]] fruit_df = pd.DataFrame(list_of_fruit, columns = ['fruit','count']) fruit_df

fruit_dict = {'fruit':['apple','mango','banana'], 'count':[10, 12, 13]} fruit_df = pd.DataFrame(fruit_dict) fruit_df

sub_df = fruit_df[1:] sub_df

student_dict = {'FirstName': ['Larry', 'Moe', 'Curly'], 'LastName': ['Smith', 'Howard', 'Jones'], 'Midterm': [88.5, 67.8, 92.0], "Final":[78.9, 93.2, 65.5]} grades = pd.DataFrame(student_dict) grades

grades.iloc[:4, 1:3]

grades.loc[:3,'Midterm']

grades['GPA'] = [2.3, 2.7, 3.1]

grades.head(2)

c) Data Analysis, CSV files

Note: The following code must be updated to the full file path on your computer. The 'r' must proceed with the file name, so backslash characters are not interpreted as escape characters.

poke = pd.read_csv(r'DriveLetter\full path\Pokemon.csv') type(poke)

poke.shape

poke.info

poke.head()

poke.tail()

type(poke.index)

poke.iloc[:,1]

poke.loc[:5, "Name"]

poke.sum()

poke.mean()

poke.describe()

poke.sort_values(by="Name")

poke.set_index("Name")

filter1 = poke["Speed"] >60 filter1

filter_data = poke[filter1]

filter_data

PLEASE SEE BELOW FOR LINK TO POKEMON.CSV

https://drive.google.com/file/d/1aQV9cZbw4uNgHH1-l0ulljvv_YRPUenC/view?usp=sharing