Exploratory Data Analysis in Pandas | Python Pandas Tutorials
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Top Comments (10)
Incase you are running into an error at minute 11:12, add numeric_only = True to the corr. i.e df.corr(numeric_only = True).
Hello, at minute 24:24, I managed to reverse the range of column names using [5:13][::-1]. The expression [::-1] is used to reverse ranges and it is very useful: df2 = df.groupby('Continent')[df.columns[5:13][::-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) df2 Thank you very much, Mr. Alex, for these tutorials.
Man, “Oceania” was so funny 😂, tks for the class!
If you are having any at 16:59, that's because you are looking for the mean of other datatypes apart from numbers. To resolve this, include numeric_only=True in your mean function, as in "df.groupby('Continent').mean(numeric_only=True)"
the correction of df.corr() is: numeric_columns = df.select_dtypes(include=[np.number]) correlation_matrix = numeric_columns.corr correlation_matrix()
This is absolutely top tier content. I can't stress this enough to people new, or going into the DA/DS field: you WILL be exploring and cleaning data sets much more than you will be visualizing and building models. Thanks for this, Alex!
Oceania is one of the 7 Continents (North America, South America, Europe, Asia, Africa, Oceania, Antartica). It's basically Australia and the countries (islands) around it. Hope that helps!
We can also write this to save time writing all the column names in the list "df2 = df.groupby('Continent')[df.columns[12:4:-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) "
I just finished all the videos in you bootcamp playlist few hours ago and I'm excited to do this again..
omg I laughed out loud at the "Oceania" part ;)))) Alex is so funny and brutally honest about things he didn't know ;)))
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Top Comments (10)
Incase you are running into an error at minute 11:12, add numeric_only = True to the corr. i.e df.corr(numeric_only = True).
Hello, at minute 24:24, I managed to reverse the range of column names using [5:13][::-1]. The expression [::-1] is used to reverse ranges and it is very useful: df2 = df.groupby('Continent')[df.columns[5:13][::-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) df2 Thank you very much, Mr. Alex, for these tutorials.
Man, “Oceania” was so funny 😂, tks for the class!
If you are having any at 16:59, that's because you are looking for the mean of other datatypes apart from numbers. To resolve this, include numeric_only=True in your mean function, as in "df.groupby('Continent').mean(numeric_only=True)"
the correction of df.corr() is: numeric_columns = df.select_dtypes(include=[np.number]) correlation_matrix = numeric_columns.corr correlation_matrix()
This is absolutely top tier content. I can't stress this enough to people new, or going into the DA/DS field: you WILL be exploring and cleaning data sets much more than you will be visualizing and building models. Thanks for this, Alex!
Oceania is one of the 7 Continents (North America, South America, Europe, Asia, Africa, Oceania, Antartica). It's basically Australia and the countries (islands) around it. Hope that helps!
We can also write this to save time writing all the column names in the list "df2 = df.groupby('Continent')[df.columns[12:4:-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) "
I just finished all the videos in you bootcamp playlist few hours ago and I'm excited to do this again..
omg I laughed out loud at the "Oceania" part ;)))) Alex is so funny and brutally honest about things he didn't know ;)))