Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Let’s see the how to iterate over rows in Pandas Dataframe using
Method #1: Using the
This method iterated over the rows as (index, series) pairs.
Original DataFrame: age name 0 10 Sujeet 1 11 Sameer 2 12 Sumit Rows iterated using iterrows() : Sujeet 10 Sameer 11 Sumit 12
Method #2: Using the
This method returns a named tuple for every row.
getattr() function can be used to get the
row attribute in the returned tuple. This method is faster than Method #1.
Original DataFrame: age name 0 10 Sujeet 1 110 Sameer 2 120 Sumit Rows iterated using itertuples() : Sujeet 10 Sameer 110 Sumit 120
There are few other ways we can iterate over rows in Pandas Dataframe. See Different ways to iterate over rows in Pandas Dataframe.
- Different ways to iterate over rows in Pandas Dataframe
- Ranking Rows of Pandas DataFrame
- Get all rows in a Pandas DataFrame containing given substring
- Sorting rows in pandas DataFrame
- How to randomly select rows from Pandas DataFrame
- Create a list from rows in Pandas dataframe
- Dealing with Rows and Columns in Pandas DataFrame
- How to get rows/index names in Pandas dataframe
- Iterating over rows and columns in Pandas DataFrame
- Create a list from rows in Pandas DataFrame | Set 2
- Selecting rows in pandas DataFrame based on conditions
- Python | Delete rows/columns from DataFrame using Pandas.drop()
- Python | Pandas DataFrame.fillna() to replace Null values in dataframe
- Drop rows from the dataframe based on certain condition applied on a column
- Grouping Rows in pandas
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