Indexing and Selecting Data in Python – How to slice, dice for Pandas Series and DataFrame

guest_blog 02 Sep, 2022 • 5 min read


Indexing and Selecting Data

  • Enables automatic and explicit data alignment.
  • Allows intuitive getting and setting of subsets of the data set.


# for getting values with a boolean array
print (df.loc['a']>0)

indexing and selecting data - .loc

print df.loc[:,'B']

indexing and selecting data - .loc

The query() Method

#creating dataframe of 10 rows and 3 columns
df4 = pd.DataFrame(np.random.rand(10, 3), columns=list('abc'))

Image for post

Image for post

#with query()
df4.query('(x < b) & (b < c)')

Image for post

  • drop_duplicates: removes duplicate rows.
df5 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two'],
                    'b': ['x', 'y', 'x', 'y', 'x'],
                    'c': np.random.randn(5)})

Image for post


Image for post


Image for post

  1. Interesting 10 Machine Learning and Data Science Projects with Datasets
  2. Basic Understanding of NLP With Python


About the Author


Amit Chauhan

I am a Research Scholar and a technical person with 4-year experience in R&D Electronics. Data Science enthusiastic
Data Indexingpandaspython
guest_blog 02 Sep 2022

BeginnerData ExplorationPandasProgrammingPython

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Responses From Readers


Become a full stack data scientist