Hi guys, since its so quiet here 1

kingz2000 · May 11, 2020

Hi guys,

since its so quiet here ,let me throw in another easy one. The problem is, that I'm so used toSQL and using "Where"..So here goes..

df['artist'].value_counts().reset_index()

gives me a select count...

now how I can add a condition to only list those above a certain number, say 3?

I tried this...

df.groupby('artist').count()>3

..which just gave me my list with true and false on each column.

Thanks in advance

xwb · May 11, 2020

Is df a panda's dataframe?

kingz2000 · May 11, 2020

yes, df is the general short term for any panda dataframe

mikrom · May 11, 2020

kingz2000,
it would be helpful if you could post how your data looks like.

mikrom · May 11, 2020

I tried this:
first I created this data frame from a list of items

Code:

>>> my_list = ['foo', 'bar', 'baz', 'foo', 'bar', 'foo', 'bar', 'foo']

>>> my_df=pd.DataFrame({"item" : my_list})

>>> my_df
  item
0  foo
1  bar
2  baz
3  foo
4  bar
5  foo
6  bar
7  foo

Now I can get a list of items which occurs more than once in these steps:

Code:

>>> my_df["item"].value_counts()>1
foo     True
bar     True
baz    False
Name: item, dtype: bool

>>> my_df.loc[(my_df["item"].value_counts()>1).values]
  item
0  foo
1  bar

>>> list((my_df.loc[(my_df["item"].value_counts()>1).values])["item"])
['foo', 'bar']

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Hi guys, since its so quiet here 1

kingz2000

Programmer

xwb

Programmer

kingz2000

Programmer

mikrom

Programmer

mikrom

Programmer

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