Iteration is taking each item of something one after the other. Pandas dataframe consists of rows and columns so, for iterating over the dataframe, we iterate a dataframe like a dictionary in Python for Data Science.

In Pandas, we can iterate an element in two ways:

- Iterate over rows
- Iterate over columns

**Iterating over rows:**

For iterating over rows, we use three functions **iteritems(), iterrows(), itertuples()**. So, these will help in iteration over rows.

**Iteration over rows using iterrows():**

It is a function which returns each index value along with a series containing data in each row.

Code 1:

So, now we will see the application of **iterrows() **function for obtaining each element of rows.

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Code 2:

So, now we will apply a iterrow for getting each element of rows in dataframe.

OUTPUT:

**Iterating over rows using iteritems():**

For iterating over rows, we use the **iteritems() **function. It is a function which iterates over each column as key, value pair with label as key and the column value as series object.

Code 1:

So, we will apply **iteritems() **function for retrieving rows of dataframe.

OUTPUT:

Code 2:

OUTPUT:

So, now we will see the application of **iteritems() **for retrieving rows from a dataframe.

OUTPUT:

**Iterating over rows using itertuples():**

It is a function which returns a tuple for each row in the dataframe. So, the first element of tuple will be the row’s corresponding index value and the remaining values are the row values.

Code 1:

So, now we will apply **itertuples() **function to get tuple for each row.

OUTPUT:

Code 2:

So, now we will see application of **itertuples() **for getting tuple of each rows.

OUTPUT:

**Iterating over columns:**

For iterating over columns we should create a list of dataframe columns. So, we will iterate through that list for pulling out the dataframe columns.

Code 1:

OUTPUT:

Code 2:

OUTPUT:

So, to learn more about iterations in python for data science, you can check this and this as well.