dataframe unique values in each column
for col in df:
print(df[col].unique())dataframe unique values in each column
for col in df:
print(df[col].unique())pandas distinct
>gapminder['continent'].unique()
array(['Asia', 'Europe', 'Africa', 'Americas', 'Oceania'], dtype=object)dataframe python unique values rows
# get the unique values (rows)
df.drop_duplicates()python - subset dataframe based on unique value of a clumn
# Keep first duplicate value
my_df = my_df.drop_duplicates(subset=['my_var'])
# Keep last duplicate value
my_df = my_df.drop_duplicates(subset=['my_var'], keep='last')
# Remove all duplicate values
my_df = my_df.drop_duplicates(subset=['my_var'], keep=False)unique entries in column pandas
# Import modules
import pandas as pd
# Set ipython's max row display
pd.set_option('display.max_row', 1000)
# Set iPython's max column width to 50
pd.set_option('display.max_columns', 50)
# Create an example dataframe
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 2012, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
#List unique values in the df['name'] column
df.name.unique()unique rows in dataframe
In [33]: df[df.columns[df.apply(lambda s: len(s.unique()) > 1)]]
Out[33]:
A B
0 0 a
1 1 b
2 2 c
3 3 d
4 4 eCopyright © 2021 Codeinu
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