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I have a Pandas DataFrame like following:
A B C 0 192.168.2.85 192.168.2.85 124.43.113.22 1 192.248.8.183 192.248.8.183 192.168.2.85 2 192.168.2.161 NaN 192.248.8.183 3 66.249.74.52 NaN 192.168.2.161 4 NaN NaN 66.249.74.52 I want to get the count of a certain values across columns. So my expected output is something like:
IP Count 192.168.2.85 3 #Since this value is there in all coulmns 192.248.8.183 3 192.168.2.161 2 66.249.74.52 2 124.43.113.22 1 I know how to this across rows, but doing this for columns is bit strange?Help me to solve this? Thanks.
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The value_counts() method can be applied to either a Pandas Series or DataFrame. The method counts the number of times a value appears. The method can convert the values into a normalized percentage, using the normalize=True argument. The method can be applied to multiple columns to establish a hierarchy between ...
How do you Count the Number of Occurrences in a data frame? To count the number of occurrences in e.g. a column in a dataframe you can use Pandas value_counts() method. For example, if you type df['condition']. value_counts() you will get the frequency of each unique value in the column “condition”.
Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element.
stack it first and then use value_counts:
In [14]: df.stack().value_counts() Out[14]: 192.248.8.183 3 192.168.2.85 3 66.249.74.52 2 192.168.2.161 2 124.43.113.22 1 dtype: int64 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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