In this article, we will look at how to get the solution for the problem, Python Dataframe Add Rank Column With Code Examples

DENSE_RANK computes the rank of a row in an ordered group of rows and returns the rank as a NUMBER . The ranks are consecutive integers beginning with 1. The largest rank value is the number of unique values returned by the query. Rank values are not skipped in the event of ties.

```
df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
Animal Number_legs default_rank max_rank NA_bottom pct_rank
0 cat 4.0 2.5 3.0 2.5 0.625
1 penguin 2.0 1.0 1.0 1.0 0.250
2 dog 4.0 2.5 3.0 2.5 0.625
3 spider 8.0 4.0 4.0 4.0 1.000
4 snake NaN NaN NaN 5.0 NaN
```

We will use the rank() function with the argument pct = True to find the percentile rank.

=RANK(number,ref,[order]) The RANK function uses the following arguments: Number (required argument) – This is the value for which we need to find the rank. Ref (required argument) – Can be a list of, or an array of, or reference to, numbers.

For DataFrame objects, rank only numeric columns if set to True.How to rank NaN values:

- keep: assign NaN rank to NaN values.
- top: assign lowest rank to NaN values.
- bottom: assign highest rank to NaN values.

In Python, there are two ways, sort() and sorted() , to sort lists ( list ) in ascending or descending order. If you want to sort strings ( str ) or tuples ( tuple ), use sorted() .

- Step 1 - Loading Library. We have imported numpy which is needed.
- Step 2 - Creating a Matrix. We have created a matrix by using np.array with different values in it.
- Step 3 - Calculating Rank. We have calculated rank of the matrix by using numpy function np.linalg.matrix_rank and passing the matrix through it.

For assigning the rank to our dataframe's elements, we use a built-in function of the pandas library that is the . rank() function. We pass the criteria based on which we are ranking the elements to it, and this function returns a new column in each row where the ranking is stored.

To rank the rows of Pandas DataFrame we can use the DataFrame. rank() method which returns a rank of every respective index of a series passed.

One way of renaming the columns in a Pandas Dataframe is by using the rename() function. This method is quite useful when we need to rename some selected columns because we need to specify information only for the columns which are to be renamed.

The rank() function is used to compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values.

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