<jupyter_start><jupyter_text>Lambda School Data Science - A First Look at Data  Lecture - let's explore Python DS libraries and examples!The Python Data Science ecosystem is huge. You've seen some of the big pieces - pandas, scikit-learn, matplotlib. What parts do you want to see more of?<jupyter_code>2 + 2
def helloworld():
  return print('Hello World!')
helloworld()
import pandas as pd

df = pd.DataFrame({'a': [1, 2, 3, 4, 5], 
                  'b': [5, 4, 3, 2, 1]})
df.head()
df.plot.scatter('a', 'b');
joe = {'name': 'Joe', 'is_female': False, 'age': 19}
alice = {'name': 'Alice', 'is_female': True, 'age': 20}
sarah = {'name': 'Sarah', 'is_female': True, 'age': 20}
students = [joe, alice, sarah]
import numpy as np
np.random.randint(0, 10, size=10)
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [2, 4, 6, 10]
print(x, y)
plt.scatter(x, y, color='r');
plt.plot(x, y, color='g');
df = pd.DataFrame({'first_col': x, 'second_col': y})
df
df['first_col']
df['second_col']
df.shape
df['third_col'] = df['first_col'] + 2*df['second_col']
df
df.shape
arr_1 = np.random.randint(low=0, high=100, size=10000)
arr_2 = np.random.randint(low=0, high=100, size=10000)
arr_1.shape
arr_2.shape
arr_1 + arr_2
x + y
type(arr_1)
type(arr_2)
type(x)
df
df['fourth_col'] = df['third_col'] > 10
df
df.shape
df[df['second_col'] < 10]
print(students)
df_1 = pd.DataFrame(students)
df_1
df_1['legal_drinker'] = df_1['age'] > 21
df_1
df_1[df_1['name'] == 'Alice']<jupyter_output><empty_output><jupyter_text>Assignment - now it's your turnPick at least one Python DS library, and using documentation/examples reproduce in this notebook something cool. It's OK if you don't fully understand it or get it 100% working, but do put in effort and look things up.<jupyter_code># TODO - your code here
# Use what we did live in lecture as an example

# 1.
# Above I recreated what <NAME> did yesterday in lecture. I decided to look
# at the day one notes where they create a dictionary of three peoples names. 
# I decided to add some columns with legal drinking status (worked) and one with
# the eligibility of said person on playing for the United States Womens National 
# Soccer Team (didn't work). I couldn't get it to read a boolean (pass/fail) 
# so I would like to figure that out. 

# 2.
# I believe the boolean aspect will be the hardest. Also, changing some of the 
# data types is going to be challenging.

# 3. 
# I'm really hoping that about halfway through the NFL season I will be able
# to use this to predict where the teams currently sit then. So, appending a 
# data frame based off of previous data.

# 4.
# I would like to continue exploring dataframes. The idea of a large subset of 
# values that can be quickly explored with key words then used to give a 
# summation of that data. I would like to be able to predict possible values of 
# players versus teams in order to offer insight into personel decisions.<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11728813559322034, 'fraction_numerical': 0.026779661016949154, 'mean_word_length': 4.394881170018282, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '10994', 'license': 'MIT', 'n_tokens_mistral': 1072, 'n_tokens_neox': 988, 'n_words': 416, 'path': 'module1-afirstlookatdata/Kole_Goldsberry_LS_DSPT3_111_A_First_Look_at_Data.ipynb', 'repo_name': 'Tenntucky/DS-Unit-1-Sprint-1-Dealing-With-Data', 'size': '2952'}