print the unique values in every column in a pandas dataframe

Question

I have a dataframe (df) and want to print the unique values from each column in the dataframe.

I need to substitute the variable (i) [column name] into the print statement

column_list = df.columns.values.tolist() for column_name in column_list:     print(df."[column_name]".unique() 

Update

When I use this: I get "Unexpected EOF Parsing" with no extra details.

column_list = sorted_data.columns.values.tolist() for column_name in column_list:       print(sorted_data[column_name].unique() 

What is the difference between your syntax YS-L (above) and the below:

for column_name in sorted_data:       print(column_name)       s = sorted_data[column_name].unique()       for i in s:         print(str(i)) 

Answer 1

It can be written more concisely like this:

for col in df:     print(df[col].unique()) 

Generally, you can access a column of the DataFrame through indexing using the [] operator (e.g. df['col']), or through attribute (e.g. df.col).

Attribute accessing makes the code a bit more concise when the target column name is known beforehand, but has several caveats -- for example, it does not work when the column name is not a valid Python identifier (e.g. df.123), or clashes with the built-in DataFrame attribute (e.g. df.index). On the other hand, the [] notation should always work.