What you should Have to Start

Lesson Portion 1: ReIntroduction to Data Analysis, NunPy, and Pandas, Why is it important?

Data Analysis.

  • Data Analysis is the process of examining data sets in order to find trends and draw conclusions about the given information. Data analysis is important because it helps businesses optimize their performances.

What is NunPy and Pandas

  • Pandas library involves a lot of data analysis in Python. NumPy Library is mostly used for working with numerical values and it makes it easy to apply with mathematical functions.
  • Imagine you have a lot of toys, but they are all mixed up in a big box. NumPy helps you to put all the same types of toys together, like all the cars in one pile and all the dolls in another. Pandas is like a helper that helps you to remember where each toy is located. So, if you want to find a specific toy, like a red car, you can ask Pandas to find it for you.
  • Just like how it's easier to find a toy when they are sorted and organized, it's easier for grown-ups to understand and analyze big sets of numbers when they use NumPy and Pandas.

Lesson Portion 2 More into NunPy

What we are covering;

  • Explanation of NumPy and its uses in data analysis
  • Importing NumPy library
  • Examining NumPy arrays
  • Creating NumPy arrays and performing intermediate array operations
  • Popcorn Hacks, Make your own percentile NunPy array

What is NunPy's use in data analysis/ how to import NunPy.

NumPy is a tool in Python that helps with doing math and data analysis. It's great for working with large amounts of data, like numbers in a spreadsheet. NumPy is really good at doing calculations quickly and accurately, like finding averages, doing algebra, and making graphs. It's used a lot by scientists and people who work with data because it makes their work easier and faster.

import numpy as np

List of NunPy Functions, what they do, and examples.

Example of Using NunPy in Our Project

This code calculates the total plate appearances for a baseball player using NumPy's sum() function, similar to the original example. It then uses NumPy to calculate the total number of bases (hits plus walks) for the player, and divides that by the total number of plate appearances to get the on-base percentage. The results are then printed to the console.

import numpy as np

# Example data
player_hits = np.array([3, 1, 2, 0, 1, 2, 1, 2])  # Player's hits in each game
player_walks = np.array([1, 0, 0, 1, 2, 1, 1, 0])  # Player's walks in each game
player_strikeouts = np.array([2, 1, 0, 2, 1, 1, 0, 1])  # Player's strikeouts in each game

# array to store plate appearances (PA) for the player
total_pa = np.sum(player_hits != 0) + np.sum(player_walks) + np.sum(player_strikeouts)

# array to store on-base percentage (OBP) for the player
total_bases = np.sum(player_hits) + np.sum(player_walks)
obp = total_bases / total_pa

# Print the total plate appearances and on-base percentage for the player
print(f"Total plate appearances: {total_pa}")
print(f"On-base percentage: {obp:.3f}")
Total plate appearances: 21
On-base percentage: 0.857

Activity 1; PopCorn Hacks; Creating a NunPy Array and Analyzing the Data using Array Operations

import numpy as np

#Create a NumPy array of the heights of players in a basketball team
heights = np.array([192, 195, 193, 200, 211, 199, 201, 198, 184, 190, 196, 203, 208, 182, 207])

# Calculate the percentile rank of each player's height
percentiles = np.percentile(heights, [25, 50, 75])

# Print the results
print("The 25th percentile height is", percentiles[0], "cm.")
print("The 50th percentile height is", percentiles[1], "cm.")
print("The 75th percentile height is", percentiles[2], "cm.")

# Determine the number of players who are in the top 10% tallest
top_10_percent = np.percentile(heights, 90)
tallest_players = heights[heights >= top_10_percent]

print("There are", len(tallest_players), "players in the top 10% tallest.")
The 25th percentile height is 192.5 cm.
The 50th percentile height is 198.0 cm.
The 75th percentile height is 202.0 cm.
There are 2 players in the top 10% tallest.
import numpy as np

#Create a NumPy array of the x
gamescores = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Calculate the percentile rank of x
y = np.percentile(gamescores, [1,2,3])

# Print the results
print("", percentiles[0], "")
print("", percentiles[1], "")
print("", percentiles[2], "")

# Determine the number of players who are in the top 10% x
t = np.percentile(gamescores, 90)
z = gamescores[gamescores >= t]

print("There are", len(z), "players in the top 10% (gamescores).")
 192.5 
 198.0 
 202.0 
There are 1 players in the top 10% (gamescores).

Lesson Portion 3 More into Pandas

What we are Covering

  • Explanation of Pandas and its uses in data analysis
  • Importing Pandas library
  • Loading data into Pandas DataFrames from CSV files
  • Manipulating and exploring data in Pandas DataFrames
  • Example of using Pandas for data analysis tasks such as filtering and sorting

What are pandas and what is its purpose?

  • Pandas is a software library that is used in Python
  • Pandas are used for data analysis and data manipulation
  • Pandas offer data structures and operations for manipulating numerical tables and time series.
  • Pandas is free

Things you can do using pandas

  • Data Cleansing; Identifying and correcting errors, inconsistencies, and inaccuracies in datasets.
  • Data fill; Filling in missing values in datasets.
  • Statistical Analysis; Analyzing datasets using statistical techniques to draw conclusions and make predictions.
  • Data Visualization; Representing datasets visually using graphs, charts, and other visual aids.
  • Data inspection; Examining datasets to identify potential issues or patterns, such as missing data, outliers, or trends.

Pandas and Data analysis

The 2 most important data structures in Pandas are:

  • Series ; A Series is a one-dimensional labeled array that can hold data of any type (integer, float, string, etc.). It is similar to a column in a spreadsheet or a SQL table. Each element in a Series has a label, known as an index. A Series can be created from a list, a NumPy array, a dictionary, or another Pandas Series.
  • DataFrame ;A DataFrame is a two-dimensional labeled data structure that can hold data of different types (integer, float, string, etc.). It is similar to a spreadsheet or a SQL table. Each column in a DataFrame is a Series, and each row is indexed by a label, known as an index. A DataFrame can be created from a dictionary of Series or NumPy arrays, a list of dictionaries, or other Pandas DataFrame.

Dataframes

import pandas as pd
pd.__version__
'1.4.2'

Importing CSV Data

  • imports the Pandas library and assigns it an alias 'pd'.
  • Loads a CSV file named 'nba_player_statistics.csv' into a Pandas DataFrame called 'df'.
  • Specifies a player's name 'Jimmy Butler' to filter the DataFrame for that player's stats. It creates a new DataFrame called 'player_stats' which only contains rows where the 'NAME' column matches 'Jimmy Butler'.
  • Displays the player's stats for points per game (PPG), rebounds per game (RPG), and assists per game (APG) using the print() function and string formatting.
  • The code uses the double square brackets [[PPG', 'RPG', 'APG']] to select only the columns corresponding to the player's points per game, rebounds per game, and assists per game from the player_stats DataFrame.
  • In summary, the code loads NBA player statistics data from a CSV file, filters it for a specific player, and displays the stats for that player's PPG, RPG, and APG using a Pandas DataFrame.
import pandas as pd
# Load the CSV file into a Pandas DataFrame
df = pd.read_csv('/Users/lunaiwazaki/vscode/iwazaki/_notebooks/files/nba_player_statistics.csv')
# Filter the DataFrame to only include stats for a specific player (in this case, Jimmy Butler)
player_name = 'Jimmy Butler'
player_stats = df[df['NAME'] == player_name]
# Display the stats for the player
print(f"\nStats for {player_name}:")
print(player_stats[['PPG', 'RPG', 'APG']])
Stats for Jimmy Butler:
    PPG  RPG   APG
0  35.0  5.0  11.0

In this code segment below we use Pandas to read a CSV file containing NBA player statistics and store it in a DataFrame.

The reason Pandas is useful in this scenario is because it provides various functionalities to filter, sort, and manipulate the NBA data efficiently. In this code, the DataFrame is filtered to only include the stats for the player you guys choose.

  • Imports the Pandas library and assigns it an alias 'pd'.
  • Loads a CSV file named 'nba_player_statistics.csv' into a Pandas DataFrame called 'df'.
  • Asks the user to input a player name using the input() function and assigns it to the variable player_name.
  • Filters the DataFrame for the specified player name using the df[df['NAME'] == player_name] syntax, and assigns the resulting DataFrame to the variable player_stats.
  • Checks if the player_stats DataFrame is empty using the empty attribute. If it is empty, prints "No stats found for that player." Otherwise, it proceeds to step 6.
  • Displays the player's stats for points per game (PPG), rebounds per game (RPG), assists per game (APG), and total points + rebounds + assists (P+R+A) using the print() function and string formatting.
  • In summary, this code loads NBA player statistics data from a CSV file, asks the user to input a player name, filters the DataFrame for that player's stats, and displays the player's stats for PPG, RPG, APG, and P+R+A. If the player is not found in the DataFrame, it prints a message indicating that no stats were found.
import pandas as pd
df = pd.read_csv('/Users/lunaiwazaki/vscode/iwazaki/_notebooks/files/nba_player_statistics.csv')
# Load CSV file into a Pandas DataFrame
player_name = input("Enter player name: ")
# Get player name input from user
player_stats = df[df['NAME'] == player_name]
# Filter the DataFrame to only include stats for the specified player
if player_stats.empty:
    print("No stats found for that player.")
else:
# Check if the player exists in the DataFrame
    print(f"\nStats for {player_name}:")
print(player_stats[['PPG', 'RPG', 'APG', 'P+R+A']])
# Display the stats for the player inputted.
Stats for LeBron James:
     PPG   RPG  APG  P+R+A
26  21.0  11.0  5.0   37.0

Lesson Portion 4

What we will be covering

  • Example of analyzing data using both NumPy and Pandas libraries
  • Importing data into NumPy and Pandas Performing basic data analysis tasks such as mean, median, and standard deviation Visualization of data using Matplotlib library

Example of analyzing data using both NumPy and Pandas libraries

import numpy as np
import pandas as pd

# Load CSV file into a Pandas DataFrame

df = pd.read_csv('/Users/lunaiwazaki/vscode/iwazaki/_notebooks/files/nba_player_statistics.csv')

# Filter the DataFrame to only include stats for the specified player

player_name = input("Enter player name: ")
player_stats = df[df['NAME'] == player_name]
if player_stats.empty:
    print("No stats found for that player.")
else:

    # Convert the player stats to a NumPy array
    player_stats_np = np.array(player_stats[['PPG', 'RPG', 'APG', 'P+R+A']])

    # Calculate the average of each statistic for the player

    player_stats_avg = np.mean(player_stats_np, axis=0)

    # Print out the average statistics for the player

    print(f"\nAverage stats for {player_name}:")
    print(f"PPG: {player_stats_avg[0]:.2f}")
    print(f"RPG: {player_stats_avg[1]:.2f}")
    print(f"APG: {player_stats_avg[2]:.2f}")
    print(f"P+R+A: {player_stats_avg[3]:.2f}")
Average stats for LeBron James:
PPG: 21.00
RPG: 11.00
APG: 5.00
P+R+A: 37.00

NumPy impacts the given code because it performs operations on arrays efficiently. Specifically, it converts a Pandas DataFrame object to a NumPy array object, and then calculates the average statistics for a the player you guys inputted. Without NumPy, it would be more difficult and less efficient to perform these calculations on large data sets. It does the math for us.

Importing data into NumPy and Pandas Performing basic data analysis tasks such as mean, median, and standard deviation Visualization of data using Matplotlib library

Matplotlib is used essentially to create visuals of data. charts,diagrams,etc.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load the CSV file into a Pandas DataFrame
df = pd.read_csv('/Users/lunaiwazaki/vscode/iwazaki/_notebooks/files/nba_player_statistics.csv')

# Print the first 5 rows of the DataFrame
print(df.head())

# Calculate the mean, median, and standard deviation of the 'Points' column
mean_minutes = df['MPG'].mean()
median_minutes = df['MPG'].median()
stddev_minutes = df['MPG'].std()

# Print the results
print('Mean Minutes: ', mean_minutes)
print('Median Minutes: ', median_minutes)
print('Standard Deviation Minutes: ', stddev_minutes)

# Create a histogram of the 'Points' column using Matplotlib
plt.hist(df['MPG'], bins=20)
plt.title('MPG Histogram')
plt.xlabel('MPG')
plt.ylabel('Frequency')
plt.show()
   RANK             NAME TEAM POS   AGE  GP   MPG  USG%   TO%  FTA  ...   APG  \
0     1     Jimmy Butler  Mia   F  33.6   1  42.9  34.3   9.9    8  ...  11.0   
1     2    Kawhi Leonard  Lac   F  31.8   2  40.2  30.0  11.9   17  ...   6.0   
2     3  Khris Middleton  Mil   F  31.7   1  33.1  37.5  19.8   10  ...   4.0   
3     4     Devin Booker  Pho   G  26.5   2  44.1  28.8  16.2   14  ...   6.0   
4     5     De'Aaron Fox  Sac   G  25.3   2  38.2  31.6   9.0   14  ...   7.0   

   SPG  BPG  TPG   P+R   P+A  P+R+A    VI   ORtg   DRtg  
0  3.0  0.0  3.0  40.0  46.0   51.0  11.6  117.2  103.8  
1  2.0  0.5  3.0  41.0  40.5   47.0  11.0  129.5  110.4  
2  0.0  0.0  5.0  42.0  37.0   46.0  12.8  115.5  111.9  
3  2.5  1.5  4.0  33.0  38.0   39.0   5.2  121.9  111.0  
4  3.5  0.5  2.5  34.0  38.0   41.0   9.1  112.6  108.8  

[5 rows x 29 columns]
Mean Minutes:  20.985483870967748
Median Minutes:  23.0
Standard Deviation Minutes:  12.844102823170283

In this example code, we first import the necessary libraries, including NumPy, Pandas, and Matplotlib. We then load the CSV file into a Pandas DataFrame using the pd.read_csv() function. We print the first 5 rows of the DataFrame using the df.head() function. Next, we calculate the mean, median, and standard deviation of the 'MPG' column using the appropriate Pandas methods, and print the results. And, we create a histogram of the 'MPG' column using Matplotlib by calling the plt.hist() function and setting appropriate axis labels and a title. We then call the plt.show() method to display the plot. Even though NumPy is not directly used in this code, it is an important underlying component of the pandas and Matplotlib libraries, which are used to load, manipulate and visualize data. It allows them to work more efficiently

Lesson Portion 5; Summary

Summary/Goals of Lesson:

One of our goals was to make you understand data analysis and how it can be important in optimizing business performance. We also wanted to make sure you understood the use of Pandas and NumPy libraries in data analysis, with a focus on NumPy. As someone who works with data, we find Pandas incredibly useful for manipulating, analyzing, and visualizing data in Python. The way we use pandas is to calculate individual player and team statistics. We are a group that works with numerical data, so NumPy is one of our favorite tools for working with arrays and applying mathematical functions to them. It is very fast at computing and manipulating arrays making it a very valuable tool for tracking statistics which is important to our group. For example, if you have an array of the points scored by each player in a game, you can use NumPy to calculate the total points scored, average points per player, or the highest and lowest scoring players.

Lesson Portion 6 Hacks

Printing a CSV File (0.5)

  • Use this link https://github.com/ali-ce/datasets to select csv file of a topic you are interested in, or you may find one online.
  • Once you select your topic make sure it is a csv file and then you want to press on the button that says raw.
  • After that copy that information and create a file with a name and .csv at the end and paste your information.
  • Below is a start that you can use for your hacks.
  • Your goal is to print 2 specific parts from data (example could be like population and country).

Popcorn Hacks (0.2)

  • Lesson Portion 1. #### Answering Questions (0.2)
  • Found Below.

Submit By Thursday 8:35 A.M.

  • How to Submit: Slack a Blog Post that includes all of your hacks to "Joshua Williams" on Slack.
import pandas as pd
# read the CSV file
df = pd.read_csv("/Users/lunaiwazaki/vscode/iwazaki/_notebooks/files/Games.csv")
# display the data in a table
print(df)
games = input("Enter game name: ")
# Get player name input from user
games = df[df['Game'] == games]
# Filter the DataFrame to only include stats for the specified player
if games.empty:
    print("No stats found for that game.")
else:
# Check if the player exists in the DataFrame
    print(f"\nStats for {games}:")
print(games[['Game', 'Series', 'Country', 'Details', 'Ban Status']])
                                                    Id  \
0                     50 Cent: Bulletproof - Australia   
1                      Assassin's Creed - Saudi Arabia   
2                               Battlefield III - Iran   
3                               Battlefield IV - China   
4    BlazBlue II: Continuum Shift - United Arab Emi...   
..                                                 ...   
131  Tom Clancy's Splinter Cell III: Chaos Theory -...   
132                                 Voyeur - Australia   
133                  Watch Dogs - United Arab Emirates   
134                              Wolfenstein - Germany   
135                           Wolfenstein 3D - Germany   

                                             Game                      Series  \
0                            50 Cent: Bulletproof        50 Cent: Bulletproof   
1                                Assassin's Creed            Assassin's Creed   
2                                 Battlefield III                 Battlefield   
3                                  Battlefield IV                 Battlefield   
4                    BlazBlue II: Continuum Shift                    BlazBlue   
..                                            ...                         ...   
131  Tom Clancy's Splinter Cell III: Chaos Theory  Tom Clancy's Splinter Cell   
132                                        Voyeur                      Voyeur   
133                                    Watch Dogs                  Watch Dogs   
134                                   Wolfenstein                 Wolfenstein   
135                                Wolfenstein 3D                 Wolfenstein   

                  Country                                            Details  \
0               Australia  Banned due to high impact gory violence.\nA ce...   
1            Saudi Arabia  Was banned (for two weeks) because of perceive...   
2                    Iran  Banned due to the intense battles of the ficti...   
3                   China  Banned due to the discrediting of China's nati...   
4    United Arab Emirates  Banned likely due to suggestive and revealing ...   
..                    ...                                                ...   
131           South Korea  Banned until 2006 because one of the levels ha...   
132             Australia  Originally rated MA15+.\nLater appealed and ba...   
133  United Arab Emirates  Initially banned, and also withheld from regio...   
134               Germany                 Banned because of Nazi references.   
135               Germany             Was banned because of Nazi references.   

                                          Ban Category  \
0                         Graphic Violence and Cruelty   
1                 Ideological and/or Religious Reasons   
2                 Ideological and/or Religious Reasons   
3                 Ideological and/or Religious Reasons   
4                                       Sex and Nudity   
..                                                 ...   
131               Ideological and/or Religious Reasons   
132                                     Sex and Nudity   
133  Sex and Nudity|Graphic Violence and Cruelty|Dr...   
134               Ideological and/or Religious Reasons   
135               Ideological and/or Religious Reasons   

                    Ban Status  \
0    Censored Version Released   
1                   Ban Lifted   
2                       Active   
3                       Active   
4                       Active   
..                         ...   
131                     Active   
132                     Active   
133                     Active   
134                     Active   
135                     Active   

                                     Wikipedia Profile  \
0    https://en.wikipedia.org/wiki/50_Cent:_Bulletp...   
1       https://en.wikipedia.org/wiki/Assassin's_Creed   
2          https://en.wikipedia.org/wiki/Battlefield_3   
3          https://en.wikipedia.org/wiki/Battlefield_4   
4    https://en.wikipedia.org/wiki/BlazBlue:_Contin...   
..                                                 ...   
131  https://en.wikipedia.org/wiki/Tom_Clancy's_Spl...   
132  https://en.wikipedia.org/wiki/Voyeur_(video_game)   
133           https://en.wikipedia.org/wiki/Watch_Dogs   
134  https://en.wikipedia.org/wiki/Wolfenstein_(200...   
135       https://en.wikipedia.org/wiki/Wolfenstein_3D   

                                                 Image  \
0    //upload.wikimedia.org/wikipedia/en/2/28/50_Ce...   
1    //upload.wikimedia.org/wikipedia/en/thumb/2/2a...   
2    //upload.wikimedia.org/wikipedia/en/6/69/Battl...   
3    //upload.wikimedia.org/wikipedia/en/thumb/7/75...   
4    //upload.wikimedia.org/wikipedia/en/8/8c/BlazB...   
..                                                 ...   
131  //upload.wikimedia.org/wikipedia/en/1/17/Tom_C...   
132                                                NaN   
133  //upload.wikimedia.org/wikipedia/en/thumb/d/d9...   
134  //upload.wikimedia.org/wikipedia/en/e/ee/Wolfe...   
135  //upload.wikimedia.org/wikipedia/en/0/05/Wolfe...   

                                               Summary  \
0    50 Cent: Bulletproof is a video game for the P...   
1    Assassin's Creed is a historical fiction actio...   
2    Battlefield 3 is a first-person shooter video ...   
3    Battlefield 4 is a first-person shooter video ...   
4    BlazBlue: Continuum Shift (ブレイブルー コンティニュアム シフト...   
..                                                 ...   
131  Tom Clancy's Splinter Cell: Chaos Theory is a ...   
132  Voyeur and Voyeur II were full motion video ga...   
133  Watch Dogs (stylized as WATCH_DOGS) is an upco...   
134  Wolfenstein is a first-person shooter video ga...   
135  Wolfenstein 3D is a first-person shooter video...   

                                             Developer  \
0        Interscope Records|High Voltage|Genuine Games   
1                    Ubisoft|Griptonite Games|Gameloft   
2                                      Electronic Arts   
3                                      Electronic Arts   
4                                           Arc System   
..                                                 ...   
131                                   Gameloft|Ubisoft   
132  Philips POV Entertainment Group|InterWeave Ent...   
133                                            Ubisoft   
134                         Id Software|Raven Software   
135  Id Software|Rebecca Heineman|Ninjaforce|Stalke...   

                                             Publisher  \
0                                 Sierra|Vivendi Games   
1                                              Ubisoft   
2                                 Electronic Arts|Sega   
3                                      Electronic Arts   
4                               Arc System Works|Aksys   
..                                                 ...   
131                                   Ubisoft|Gameloft   
132            MacPlay|Philips|Interplay Entertainment   
133                                            Ubisoft   
134                                         Activision   
135  Apogee|Manaccom|Interplay Entertainment|BAM! E...   

                                           Genre  \
0                                         Action   
1    Historical fiction|Stealth|Action|Adventure   
2                           First-person shooter   
3                           First-person shooter   
4                                       Fighting   
..                                           ...   
131                                      Stealth   
132                                  Full Motion   
133                             Action|Adventure   
134                         First-person shooter   
135                         First-person shooter   

                                              Homepage  
0                                                  NaN  
1                        http://www.assassinscreed.com  
2              http://www.battlefield.com/battlefield3  
3             http://www.battlefield.com/battlefield-4  
4                               http://blazblue.jp/ac/  
..                                                 ...  
131                                                NaN  
132                                                NaN  
133                      http://www.watchdogs.ubi.com/  
134                                                NaN  
135  http://www.idsoftware.com/games/wolfenstein/wo...  

[136 rows x 14 columns]

Stats for                                  Id                  Game  \
0  50 Cent: Bulletproof - Australia  50 Cent: Bulletproof   

                 Series    Country  \
0  50 Cent: Bulletproof  Australia   

                                             Details  \
0  Banned due to high impact gory violence.\nA ce...   

                   Ban Category                 Ban Status  \
0  Graphic Violence and Cruelty  Censored Version Released   

                                   Wikipedia Profile  \
0  https://en.wikipedia.org/wiki/50_Cent:_Bulletp...   

                                               Image  \
0  //upload.wikimedia.org/wikipedia/en/2/28/50_Ce...   

                                             Summary  \
0  50 Cent: Bulletproof is a video game for the P...   

                                       Developer             Publisher  \
0  Interscope Records|High Voltage|Genuine Games  Sierra|Vivendi Games   

    Genre Homepage  
0  Action      NaN  :
                   Game                Series    Country  \
0  50 Cent: Bulletproof  50 Cent: Bulletproof  Australia   

                                             Details  \
0  Banned due to high impact gory violence.\nA ce...   

                  Ban Status  
0  Censored Version Released  

Question Hacks;

What is NumPy and how is it used in data analysis?

NumPy is a Python library used for math and data analysis. It provides efficient mathematical operations on arrays, such as algebra and graphing. It is commonly used in data analysis for array manipulation, indexing, sorting, and filtering.

What is Pandas and how is it used in data analysis?

Pandas is a Python library that has tools for data structures and analysis. It has objects for manipulating data tables and manipulating arrays. It is used in data analysis for data cleaning, merging, and visualization.

How is NunPy different than Pandas for data analysis?

NumPy is more geared towards mathematical numerical computations, and Pandas is more geared towards data manipulation and analysis. One is math, one is data analysis.

What is a DataFrame?

A Dataframe is data structure in Pandas that looks like a table or spreadsheet. It was rows and columns, holding data by column.

What are some common operations you can perform with NunPy?

With NumPy, you can calculate percentiles, average, standard deviation, and correlation.

How Can You Incorporate Either of these Data Analysis Tools (NunPy, Pandas) into your project?

I can use Pandas, in my reciepie for friends project, to provide a visual presentation. I can also use NumPy to make the calculations of the amount of reciepes each user has and the amount of alergies each receipt has.