<jupyter_start><jupyter_text>candybars = pd.read_excel('foods.xlsx', sheet_name='chocolate')candybars<jupyter_code>df = pd.read_csv('cereal.csv', index_col="name") # To use this in Jupyter lab, the data file must be in the same directory as the ipynb
df.head() # returns the first entries in the data fram according to the arguement
df.shape #prints the number of rows, cols
one=6 ** 2 + 16 / 4 * (3 - 1) % 5
one

for i in range (20,10,-2):
    print(i)
mylist = ['a', 'b', 'c', 'd', 'e']
mylist[1:3]
word='watermelon'
for i in range(len(word)):
    if i % 2 == 0: 
         print(word[i])
combo_meals = {1: ["hamburger", "fries"], 2: ["hamburger", "fries", "soda"],4: ["veggie burger", "salad", "soda"], 6: ["hot dog", "apple slices", "orange j"]}

print(combo_meals.get(3, ["hamburger", "fries"]))
x={'s':2,"d":3}
x[0]

no ='Avoid at all costs'
yes = "This one was fun"
definitely = "Outstanding!"
question=int(input("What is the rating?:"))
huh = "You must type an interger rating between 1 and 10"


if (question <=5) and (question >=0):
    print(no)
elif (question >5) and (question<9):
    print(yes)
elif (question <=10) and (question>=9): 
    print(definitely)
else:
    print(huh) #This does will not deal with string entries - an error will result<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17455391776570986, 'fraction_numerical': 0.027152831652443754, 'mean_word_length': 4.682819383259912, 'pattern_counts': {'":': 1, '<': 8, '<?xml version=': 0, '>': 8, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '321285', 'license': 'MIT', 'n_tokens_mistral': 513, 'n_tokens_neox': 488, 'n_words': 158, 'path': 'summary worksheet/Pandas_Intro.ipynb', 'repo_name': 'Lavendulaa/programming-in-python-for-data-science', 'size': '1289'}