I'm glad it helped! Groupby was always a sore spot for me learning, but now that I know it I use it all the time.

Thanks for your kind words Imad Uddin!

Perfect! I'm glad it had what you needed.

You're welcome!

You're welcome!

Thanks!

You're welcome! 😀

Using bins=3 as a parameter to the pd.cut() function automatically divides the group into 3 equally sized categories. See my comment to Xuan Tran for an explanation of how you can find out what it does or what you could do differently.

​@Adeel KhanI can think of 3 approaches to this: - Group by age_bins, then take the minimum and maximum age: df.groupby(['age_bins']).['age'].agg(['min', 'max']) - Use retbins=True in the pd.cut() function; I think retbins returns the bounds of your bins. - Define the bins yourself, i.e. bins=[0, 20, 60, 120] (instead of bins=3 as in the video) will divide the passengers into a 60 bin

.sort_values(ascending=False)

Hey Cole DD, sometimes when you read in your data pandas thinks the data is a string even though it should be integers or floats. This video here kzfaq.info/get/bejne/m9x7jNyEsbneqZ8.html discusses how to convert datatypes of columns and some common problems that you may run into when doing so. Let me know if that works.

I'm not sure I understand your question. Are you looking to filter the dataframe so that only pclass = 1 is contained in the dataframe? You could use a boolean mask pclass1 = df[df['pclass'] == 1]. If that's what you are looking for you can check out this video on filtering which I think you will find helpful kzfaq.info/get/bejne/pM9pocplr9-Ximw.html

Hi F Ashaikh, is it possible for you to email me your data (or provide me with some made up data that is similar to the data you have). That will help me see what is going on a little better. My email is bradonvalgardson@gmail.com

I did , thank you very much for your help.

@Chart Explorers*

Hi Michael, good question. The age bins was were grouped with the pandas cut method. By default the cut method will turn continuous data into categorical data by grouping it into three bins (you can specify how many bins you want - but if you don't it will make three bins). So if you have 12 values it will create three bins with 4values in each bin. pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html

Maybe this will allow you to specify the ranges of the bins. The length of the labels have to be -1 inferior with respect to the length of the bins df['age_cat'] = pd.cut(df['age'], bins=[x for x in range(0,100, 5)], labels=[x for x in range(5,100, 5)], right=True)

Hi Priti, will you run df.dtypes and let me know if there are any numeric (float or int) datatypes in your dataframe? If they are all objects check out this video on how to convert objects into numberic values kzfaq.info/get/bejne/m9x7jNyEsbneqZ8.html (hopefully that will solve your problem. If this doesn't solve your problem will you copy and past your groupby statement and send it to me please?

@@ChartExplorers # Visualize Churn Rate by Gender plot_by_gender = churn_dataset.groupby('gender').Churn.mean().reset_index() plot_data = [ go.Bar( x=plot_by_gender['gender'], y=plot_by_gender['Churn'], width = [0.3, 0.3], marker=dict( color=['orange', 'green']) ) ] plot_layout = go.Layout( xaxis={"type": "category"}, yaxis={"title": "Churn Rate"}, title='Churn Rate by Gender', plot_bgcolor = 'rgb(243,243,243)', paper_bgcolor = 'rgb(243,243,243)', ) fig = go.Figure(data=plot_data, layout=plot_layout) po.iplot(fig) This is giving me the error .Can you suggest an alternative

Great question Ivan. Try this out and see if it works for you. First I create a dictionary of data with 3 different countries and some happiness scores. Then I create a DataFrame with this data. The I use groupby function to group each country and then use apply(list) to create a list of all the values in each group. data_dict = {'country':['country_1','country_2','country_3','country_1','country_', 'country_2','country_3','country_2','country_3','country_1, 'happiness':[3,1,3,5,7,4,1,2,3,4]} df = pd.DataFrame(data_dict) df_grouped = df.groupby('country'['happiness'].apply(list)

@@ChartExplorers thank you for swift answer i managed to do it for one column but i m trying to do it for multiple columns basically just uniting rows with same country values but seperate them with comma its working when i do it for happiness score but if i try to add happiness rank it just throws out happiness score and happiness rank not values just those strings i tried as list but yea still not working I did it with this code which works for Happiness Score: frame.groupby(['Country'])['Happiness Score'].apply(lambda x:' , '.join(x.astype(str))).reset_index()

@@houndofjustice5 I think I see what you are asking. So you want to groupby country and then list out all the values for that country in the happiness and rank columns. Let me know if this works. If not, I am setting up a discord server for Chart Explorers. That might be a better medium for problem solving. # Example Data data_dict = {'country':['country_1','country_2','country_3','country_1','country_1', 'country_2','country_3','country_2','country_3','country_1'], 'happiness':[3,1,3,5,7,4,1,2,3,4], 'rank':[1,2,3,4,5,6,7,8,9,10]} df = pd.DataFrame(data_dict) # groupby with list for multiple columns df_grouped = df.groupby('country')[['happiness','rank']].agg(lambda x: list(x))

Please help as I have data of employees in which they did multiple sale, I want if any employee did sale more the 50000 againt it each emp I'd of that person print excellent rest low. Like Emp I'd. Sale status Emp1001 5000. Excellent Emp1001 45000. Excellent Emp1001 2000. Excellent Emp1002 5000. Low Emp1003 2500. Low

Hi @@SudhirKumar-ry4gk, so you are wanting to group by employee Id and for employees that had sales greater than $50,000 mark them as excellent otherwise mark them as low? Is that correct?

I'm not sure. I would need to see your data and know more context to better understand what you are trying to accomplish.

Hi Pursh, I'm not sure if I understand exactly what you are trying to accomplish. Are you trying to obtain the mean and count on groups based on multiple columns/attributes? df.groupby(['pclass','sex], as_index=False)['survived'].agg(['mean','count']) If this is the case I'm not sure the purpose of creating a function to do this.

Hi, it might be fifa.groupby(by='position').apply(lambda group: group.sort_values(by='rate', ascending=False').head(n=5)

Hi Alan, you did a great job explaining thanks providing me an example of what you have done. 😀 If I'm understanding correctly (please correct me if I'm wrong), you have 1 column that contains categories and you want to get the max value for each of those categories in every column that you have (using groupby). Here is a simple example I made that will get the max value for every column in the dataframe based on the groups in Col_4. import pandas as pd # Create practice df df = pd.DataFrame({'Col_1':[1,2,3,4,5], 'Col_2':[6,7,8,9,10], 'Col_3':[11,12,13,14,15], 'Col_4':['Group_1','Group_2','Group_1','Group_1','Group_2'] }) # groupby Col_4 (in your case use lookup) group = df.groupby('Col_4').max() group.head() You will notice here, instead of adding a list of columns to perform the groupby function on I excluded it. This will perform the operation on all the columns. In your example, you should be able to do the following to get your answer: group = df_UE5_Compatability_info.groupby('lookup').max()

@@ChartExplorers Thanks for the reply. Below is a sample dataset (made up) to try and better explain and one that is more representative to my actual dataset. df = pd.DataFrame({'lookup':['abc123','abc124','abc123','abc125','abc125'], 'Supported':['no','yes','no','yes','yes'], 'Percentage':[0.9,0.6,0.6,0.7,0.6], 'Number of features':[1,6,10,8,11], 'Platform':['Release 1.0','Release 1.0','Release 2.0','Release 1.0','Release 2.0'] }) The output should look like the following: lookup Supported Percentage Number of features Platform 0 abc123 no 0.9 1 Release 1.0 1 abc124 yes 0.6 6 Release 1.0 2 abc123 no 0.6 10 Release 2.0 3 abc125 yes 0.7 8 Release 1.0 4 abc125 yes 0.6 11 Release 2.0 Column "lookup", Row 0 and 2 are common values, as are rows 3 and 4. My goal is to have one row per value in column "lookup", filtered on the highest value in column "Number of features" and all other columns values for the selected row should be shown in the output data frame. Using the following group = df.groupby('lookup').max() creates: Supported Percentage Number of features Platform lookup abc123 no 0.9 10 Release 2.0 abc124 yes 0.6 6 Release 1.0 abc125 yes 0.7 11 Release 2.0 But the percentage is wrong for rows abc123 and abc125, as its has included the highest percentage in each of the groups. My desired result is as follows:- abc123 no 0.6 10 Release 2.0 abc124 yes 0.6 6 Release 1.0 abc125 yes 0.6 11 Release 2.0 where values for columns "Supported', 'Percentage' are taken "as-is' from the dataframe row that contains the row with the highest "Number of features' In my script I am using group = df.groupby('lookup')['Number of features'].max() which returns the following, but I am missing the other columns, in this example Supported, Percentage and Platform. lookup abc123 10 abc124 6 abc125 11 Also, if I try to save the dataframe to csv, I only get the following Number of features 10 6 11 I would have expected to have this csv output? lookup Number of features abc123 10 abc124 6 abc125 11 Thanks again.. and I hope this is more descriptive?

@@apz9022 thanks for providing the example, that clarifies things a lot. If you use the same dataframe you created in your example you should be able to use the following code: new_df = pd.DataFrame(pd.DataFrame(columns=df.columns)) for item in df['lookup'].unique(): temp_df = df[df['lookup']==item] row = temp_df[temp_df['Number of features'] == temp_df['Number of features'].max()] alist.append(row) new_df = pd.concat([new_df, row], ignore_index=True) new_df Sadly, this uses a for loop. There might be another way to do this would avoid the for loop (I need to work on it a little more to get it to work - I'll let you know if I get it to work). I'm also going to look into groupby a little more. There are some cool things you can do with groupby, but this has several constraints that I do not think groupby will support. With 800 rows and 20 columns performance should not be an issue (but it's always nice to squeeze as much performance out as possible just for fun!). Hope this works. Let me know.

@@ChartExplorers Thanks.. what is "alist.append" ? I get an error stating "alist" is not defined?

@@ChartExplorers Thanks.. updated my code and its working like a charm! Thanks. One point, alist.append(row) did not work for me? I have left it out and it still seems to work. What does this do?

Good question, I should have explained this in the video. In the csv file missing data is represented with '?'. When we read in missing data into pandas we can tell it that missing data is represented by then pandas will treat it as a missing value rather than getting confused.

@@ChartExplorers oh, thank you (^ ^)