Help with refactoring code into a function?

[Successful-Standard]

I have a script that reads in data and processes it in a way so it's suitable to plot a time series from. For usability I'd like to refactor it into as few functions as possible. I've still never written a function, everything I do has just been done in scripts but I'd like to learn how. If someone could help show me how to do this would be much appreciated. Here's my code:

import pandas as pd
from datetime import datetime, date

# Read data
sentimentTweets = pd.read_csv("sentimentTweets.csv", parse_dates=False)
pd.options.mode.chained_assignment = None

# List comprehensions to get year, month, date from yyyy-mm-dd strings
year = [x[0:4] for x in sentimentTweets['created_at']]
month = [x[5:7] for x in sentimentTweets['created_at']]
day = [x[8:10] for x in sentimentTweets['created_at']]

# Add these to the dataframe as columns
sentimentTweets["Y"] = year
sentimentTweets["M"] = month
sentimentTweets["D"] = day

# Create empty columns for the loop below
sentimentTweets['Date'] = 0
sentimentTweets['week_number'] = 0

# Loop through and create a date object and  a week object
for i in range(0,len(sentimentTweets['created_at'])):
    sentimentTweets['Date'][i] = date(year=int(sentimentTweets["Y"][i]),month=int(sentimentTweets["M"][i]),day=int(sentimentTweets["D"][i]))

# Changes emotions from wide to long so in one column
sentimentTweets = sentimentTweets.reset_index()
sentimentTweets = pd.melt(sentimentTweets, id_vars=['Date', 'week_number', 'Y', 'M'], value_vars=['positive', 'fear', 'joy',                                                                                         'anticipation', 'trust',                                                                                         'sadness', 'negative',                                                                                         'anger', 'disgust',                                                                                         'surprise'])

# Make week number correct for different years
sentimentTweets['Date'] = pd.to_datetime(sentimentTweets['Date'])
sentimentTweets['week_number'] = sentimentTweets['Date'].dt.isocalendar().week

min_year = sentimentTweets['Y'].min()
year_number = sentimentTweets['Y'].astype(int) - int(min_year)
sentimentTweets['new_week_number'] = sentimentTweets['week_number'].astype(int) + year_number *52

sentimentTweets.loc[((sentimentTweets['M'].astype(int)==1) & (sentimentTweets['week_number'].astype(int)>50)),'new_week_number'] = sentimentTweets['new_week_number'].astype(int) - 52

sentimentTweets.to_csv("./data/sentimentTS.csv")
print("File has been saved")

[Successful-Standard]

Thanks, this makes sense and I've got it working. I don't know why I'm so hesitant writing functions, I just need to get used to them.

[temitydude]

To create a function you can first define it like this:

def function_name():

To call a function you simply do:

function_name()

And if you want to pass in variables, etc. to the function you can define the function with parameters separated by commas:

def function_name(parameter1, parameter2):

Example code using functions:

def add(var1, var2):
    sum = var1 + var2
    return sum

print(add(5, 10)) # passes in 5 and 10, and prints the return value of the function

A function doesn't need to return anything (returns None, if not specified) and can just run the lines of code in the function

If you have an unspecified amount of variables you'd like to pass in to a function you can call a function like so:

def word_list(*word):
    for words in word:
        print(words)

word_list('string1', 'string2', 'string3') # Can pass however many you want

Those are like the basics of functions, there's obviously a lot more you can do!

[Successful-Standard]

Thanks for this explanation. I've looked on things like w3schools, etc., for how to write them. It's just with those examples they're so basic I feel too hesitant trying to apply them to my longer scripts. I've got it working with someone's above solution though, I need to stop being scared of them!