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Given a dataframe how to find out all the columns that only have 0 as the values?
df 0 1 2 3 4 5 6 7 0 0 0 0 1 0 0 1 0 1 1 1 0 0 0 1 1 1 Expected output
2 4 0 0 0 1 0 0 
774
To select all columns except one column in Pandas DataFrame, we can use df. loc[:, df. columns != <column name>].
Use Python Pandas and select columns from DataFrames. Follow our tutorial with code examples and learn different ways to select your data today! If you have a DataFrame and would like to access or select a specific few rows/columns from that DataFrame, you can use square brackets or other advanced methods such as loc and iloc.
We need to create a Dataframe with multiple columns and rows where we will check if a column has zero values only. We will use the all () function to check whether a column contains zero value rows only.
If you have a DataFrame and would like to access or select a specific few rows/columns from that DataFrame, you can use square brackets or other advanced methods such as loc and iloc. Now suppose that you want to select the country column from the brics DataFrame.
This can be done by selecting the column as a series in Pandas. You can pass the column name as a string to the indexing operator. For example, to select only the Name column, you can write: Doing this, this returns the following:
I'd simply compare the values to 0 and use .all():
>>> df = pd.DataFrame(np.random.randint(0, 2, (2, 8))) >>> df 0 1 2 3 4 5 6 7 0 0 0 0 1 0 0 1 0 1 1 1 0 0 0 1 1 1 >>> df == 0 0 1 2 3 4 5 6 7 0 True True True False True True False True 1 False False True True True False False False >>> (df == 0).all() 0 False 1 False 2 True 3 False 4 True 5 False 6 False 7 False dtype: bool >>> df.columns[(df == 0).all()] Int64Index([u'2', u'4'], dtype=int64) >>> df.loc[:, (df == 0).all()] 2 4 0 0 0 1 0 0 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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