Data Analyst Portfolio Project | Correlation in Python | Project 4/4

Update: Alex, I just accepted my first job as a junior data analyst. This completes my 6-month journey to learn data analytics and change careers, and I could not have done it without your excellent Portfolio videos. Thank you so much for making these available to your viewers for free. After I built my portfolio, companies started taking a second look at my resume and inviting me to interviews. BEFORE the portfolio, I received ONLY rejection emails. Thank you, thank you, thank you!

If anyone else is having issues due to IntCastingNanError, I advise to try the following: df['budget'] = pd.to_numeric(df['budget'], errors='coerce').fillna(0).astype(int) df['gross'] = pd.to_numeric(df['gross'], errors='coerce').fillna(0).astype(int) it worked! :) Thank you Alex for your amazing videos!

Hello! The dataset appears to be updated on Kaggle and for anyone new, you will run into some issues that you need to fix to follow along. 1. Missing data. There are missing values opposed to this video so you will need to fix that. There are many ways to handle missing values but for the sake of time, I decided to drop all rows that have missing data. You will have about 71% of your data remaining. You will need to run the following if your dataframe is named df. df = df.dropna() 2. Extracting the year is different as the formatting is different. Running the following should extract the correct year. df['yearcorrect'] = df['released'].str.extract(pat = '([0-9]{4})').astype(int) 3. Duplicates, there aren't any in this dataset so you should be fine on that. I hope this helps anyone that is working on this and best of luck on your analytics journey!

Hey all, just a "stats" heads up/correction you might want to make for your portfolio: In this video, Alex wanted to see if the company was "correlated" with gross revenue. What he did was assign values (randomly, I think) to companies, countries, etc. Then he tried to see if those values were related to the gross revenue. Those randomly assigned values are "measuring" the company, country, etc at the Nominal scale. In other words, they're essentially just being used as a numeric "name"—the values themselves don't mean anything. What that means is that one value being higher than the other doesn't represent an increase in the thing being measured (for example, the USA was assigned a 54 and the UK was assigned a 53. Those are just names... the USA isn't one more of something than the UK). Because the values themselves don't represent anything, it doesn't make sense to do a correlation with them. Correlations tell us, as one variable increases, what happens to the other? So in the first question, as the budget increased, what happened to revenue? It increased. But with country, company, or other categorical variables, correlations don't make sense. The values for country and company are random, so the numbers that represent them going up doesn't tell us anything. It's no wonder then, that the correlations weren't large. Instead, it would make sense to do a t-test or ANOVA and compare means. In that case, the question would be, "Do some companies tend to produce higher revenue than others?" Or, "Do some countries tend to produce higher revenue?" etc. (For more discussion, see: https://www.webpages.uidaho.edu/~stevel/519/Correlation%20between%20a%20nominal%20(IV)%20and%20a%20continuous%20(DV)%20variable.html). Since this is a portfolio project and you want to show potential employers the result, maybe just take that part out—you wouldn't want to make a mistake like that in an application to a potential employer! (Alex, thanks so much for doing these videos! They're super helpful and I'm very very grateful!)

I can't wait for the beginner, intermediate, and advanced Python series by Alex the Analyst. It's what the people want, besides a happy Alex.

The dataset is updated and is not the same as the one in the video, if you guys have problems in the 'Create correct year section' you can do a split of the data to get only the year df['yearcorrect'] = df['released'].astype(str).str.split().str[2]

Honestly, you're an absolute legend. You really break down some of the technical barriers that exist for people entering the field of data science. You really are gem to the community.

Hey guys the info got updated since this video was posted. While I was going through the project I was able to google the problems as they came up. In case you guys get stumped here's what I found that works: This will drop any rows with null values df = df.dropna(how='any',axis=0) This will add the released date column into a separate column df['yearcorrect'] = df['released'].astype(str).str.split(', ').str[-1].astype(str).str[:4] Let me know if you that works for y'all

if df.corr() shows the error that a string variable can't be converted into int pass parameter df.corr(numeric_only=TRUE)

I have recently decided on becoming a data analyst and your videos are really helping me understand what i need to do and keep me motivated on that goal which will improve my life. I want to say thank you for your content and your honest helpfulness.

At 11:08, instead of printing null percent, we can use: for col in df.columns: print(df[col].isnull().value_counts(), "\n") This will print how many values are null. Cause you might have 1 missing in 10k values, and you will need high precision in decimals.

Man, thank you so much for this, I know you've put a lot of effort into this project serie and I can definitely say that i'm a huge fan of your work! Thank you for sharing this, you're helping a lot of people to pursuit their dreams! Greetings from Brazil :)

Man, thank you so much for this, I know you've put a lot of effort into this project series and I can definitely say that I'm a huge fan of your work! Thank you for sharing this, you're helping a lot of people pursue their dreams! Greetings from the UK :)

I really appreciate the fact that you did not edit out the parts were you made "mistakes" and actually fixed them.

If you are facing an error in datatype change, try the following df_copy = df.copy() df_copy['budget'] = df_copy['budget'].astype('int64') df_copy['gross'] = df_copy['gross'].astype('int64') df_copy Thank you Alex for this amazing video

At 13:40 If you are facing an error in datatype change, try the following :- df['budget'].round().astype('Int64') df['budget']=df['budget'].astype('Int64') hope it will help uh

This 4 part tutorial is pure gold! After your announcement that you were launching your version of data analyst course/certification, can’t wait for when it goes live, as to follow up in more depth for the concepts presented in this series. Really appreciate the time, dedication and quality of content you produce Alex.

Hey guys, at 46:24, we can simply assign .copy() method to our new variable if we want to use for loop to iterate over our new variable without affecting the original dataframe or df: df_numerized = df.copy() for col_name in df_numerized.columns: if(df_numerized[col_name].dtype == 'object'): df_numerized[col_name] = df_numerized[col_name].astype('category') df_numerized[col_name] = df_numerized[col_name].cat.codes df_numerized

Hi Alex, just finished the project. It’s awesome. Thanks for everything. I pray for your success in the future.

Thank you Alex, this has been a great project! You are a great teacher and this has been very helpful. Looking forward to everything you release in the future!