print the unique values in every column in a pandas dataframe

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))

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.

Most upvoted answer is a loop solution, hence adding a one line solution using pandas apply() method and lambda function.

print(df.apply(lambda col: col.unique()))

This will get the unique values in proper format:

pd.Series({col:df[col].unique() for col in df})

If you're trying to create multiple separate dataframes as mentioned in your comments, create a dictionary of dataframes:

df_dict = dict(zip([i for i in df.columns] , [pd.DataFrame(df[i].unique(), columns=[i]) for i in df.columns]))

Then you can access any dataframe easily using the name of the column:

df_dict[column name]

We can make this even more concise:

df.describe(include='all').loc['unique', :]

Pandas describe gives a few key statistics about each column, but we can just grab the 'unique' statistic and leave it at that.

Note that this will give a unique count of NaN for numeric columns - if you want to include those columns as well, you can do something like this:

df.astype('object').describe(include='all').loc['unique', :]

The code below could provide you a list of unique values for each field, I find it very useful when you want to take a deeper look at the data frame:

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

You can also sort the unique values if you want them to be sorted:

import numpy as np
for col in list(df):
    print(col)
    print(np.sort(df[col].unique()))

cu = []
i = []
for cn in card.columns[:7]:
    cu.append(card[cn].unique())
    i.append(cn)
    
pd.DataFrame( cu, index=i).T

Simply do this:

for i in df.columns:
    print(df[i].unique())

I was seeking for a solution to this problem as well, and the code below proved to be more helpful in my situation,

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

It gives something like below:

Fuel_Type
['Diesel' 'Petrol' 'CNG']


HP
[ 90 192  69 110  97  71 116  98  86  72 107  73]


Met_Color
[1 0]

Or in short it can be written as:

for val in df['column_name'].unique():
    print(val)

Even better. Here's code to view all the unique values as a dataframe column-wise transposed:

columns=[*df.columns]
unique_values={}
for i in columns:
    unique_values[i]=df[i].unique()
unique=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in unique_vals.items() ]))
unique.fillna('').T

The best way to do that:

Series.unique()

For example students.age.unique() the output will be the different values that occurred in the age column of the students' data frame.

To get only the number of how many different values:

Series.nunique()