Pandas dataframe reindex from 0

Pandas dataframe reindex from 0 DEFAULT

Pandas DataFrame.reindex

Pandas DataFrame

Introduction to Pandas DataFrame.reindex

The following article provides an outline for Pandas DataFrame.reindex. Every data structure which has labels to it will hold the necessity to rearrange the row values, there will also be a necessity to feed a new index itself into the data object based on the necessity. So from a python pandas perspective all these are indexing and rearrangement process at the row level is achieved by means of the reindex() method. The reindex method has the capability to rearrange the row values as per the sequence associated in the index and when a new index values is inserted in the sequence then all values for that particular row will be filled with None values. Along with its core capability the reindexing function offers a wide set of functionalities.



labelsThese represent the new labels and indexes that conform to the axis value which is mentioned in the axis.
indexThis is an alternative to the argument axis (mapper , axis=0), here index represents the rows of the dataframe. So every rename values which are mentioned here will be applied to the rows of the dataframe.
columnsThis is again an another alternative to the argument axis (mapper , axis=1), Here columns as the name suggests it represents the columns of the dataframe. So every rename values which are mentioned here will be applied to the column names of the dataframe.
axisThis argument represents the column or the axis upon which the Rename() function needs to be applied on. The value specified in this argument represents either a column position or a row position in the dataframe. To achieve this capability to flexibly travel over a dataframe the axis value is framed on below means {index (0), columns (1)}. Here mentioning the value of 0 to axis argument fills the rename values for each and every row in the dataframe, whereas mentioning the value of 1 in the dataframe fills the replacement values for all the columns in the dataframe.
copyRefers whether the data in the dataframe needs to be copied along with the operation which is been performed.
methodWhen any of the data need to be filled in the reindexeddataframe then the method argument is used.
  • none (default): don’t fill gaps.
  • pad / ffill: switch to next valid observation and progate last valid observation.
  • backfill / bfill: gaps are filled using next valid observation.
  •  nearest: gaps are filled with nearest valid observations.
levelIf the axis is a MultiIndex (hierarchical), count along a particular level.
fill_valueAll the missing value will be filled with the value mentioned here. This is a scalar entity and the default value is np.NaN.
toleranceFor inexact matches this represents the maximum distance between original and new labels.

Examples of Pandas DataFrame.reindex

Given below are the examples mentioned:

Example #1



Pandas DataFrame.reindex 1


  • In this example the core dataframe is first formulated. pd.dataframe() is used for formulating the dataframe. Every row of the dataframe are inserted along with their column names.
  • Once the dataframe is completely formulated it is printed on to the console. We can notice at this instance the dataframe holds details like employee number, Employee name and employee department.
  • The reindex method is used to reindex all the row values with a new or rearranged index value and print the updated dataframe onto the console.

Example #2



core dataframe


  • In this example the core dataframe is first formulated. pd.dataframe() is used for formulating the dataframe. Every row of the dataframe are inserted along with their column names.
  • Once the dataframe is completely formulated it is printed on to the console. We can notice at this instance the dataframe holds random set of numbers.
  • The reindex method is used to reindex all the row values with a new or rearranged index value and print the updated dataframe onto the console.

Example #3



Pandas DataFrame.reindex 3


  • In this example the core dataframe is first formulated. pd.dataframe() is used for formulating the dataframe. Every row of the dataframe are inserted along with their column names. Once the dataframe is completely formulated it is printed on to the console.
  • We can notice at this instance the dataframe holds random people information and the py_score value of those people. The key columns used in this dataframe are name, age, city and py-score value.
  • The reindex method is used to reindex all the row values with a new or rearranged index value and print the updated dataframe onto the console.


We can take in from the above examples on how precisely the reindex() operates in rearranging the index values associated to a dataframe.

This is a guide to Pandas DataFrame.reindex. Here we discuss the introduction to Pandas DataFrame.reindex along with respective examples for understanding better. You may also have a look at the following articles to learn more –

  1. Pandas DataFrame.mean()
  2. Pandas DataFrame.transpose()
  3. Pandas.Dropna()
  4. Pandas DataFrame.astype()

Pandas reset index – How to reset the index and convert the index to a column?

#pandas reset_index #reset index

in pandas is used to reset index of the dataframe object to default indexing (0 to number of rows minus 1) or to reset multi level index. By doing so, the original index gets converted to a column.

By the end of this article, you will know the different features of function, the parameters which can be customized to get the desired output from the function. This also covers use cases that are closely related to doing reset index in pandas.



    • pandas.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill= ”)


    • Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels


    • level:
          • int, str, tuple or list, (default None) Only remove the provided levels from the index. Removes all the levels by default.
    • drop:
          • bool, (default False) Do not add the old index into dataframe. By default, it adds.
    • inplace:
          • bool, (default False) Do the changes in the current datafame object
    • col_level:
          • int or str, (default 0) If the columns have multiple levels, determines at which level the labels are to be inserted. By default, it is inserted into the first level (0).
    • col_fill:
          • object, (default ”) If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.


    • DataFrame or None, DataFrame with the new index or None if inplace=True

1. How to reset the index?

To reset the index in pandas, you simply need to chain the function with the dataframe object.

Step 1: Create a simple DataFrame

pandas reset_index

Step 2: Reset the index

pandas reset_index

On applying the function, the index gets shifted to the dataframe as a separate column. It is named as . The new index of the dataframe is now integers ranging from 0 to the length of the dataframe.

2. What happens if a named index is reset?

For dataframe with named index, then, the name of the index will be made as a column name in the dataframe, instead of the default name . A named index means the index has a name assigned to it.

Step 1: Create a DataFrame with Named Index

pandas reset_index

Step 2: Reset the Index

Resetting the index in this case returns a dataframe with as the column name for the old index:-

pandas reset_index

3. How to persist the change?

Consider a dataframe below, where the index has been reset:

pandas reset_index

The output above shows that the index of the dataframe has been changed. But if you check the dataframe, it was not applied permanently:

pandas reset_index

If you want your to retain your changes, then you need to pass a parameter called , and set it’s value to , so that your index reset is applied to the dataframe object at the time of running the function.

pandas reset_index

4. How to drop the old index?

You might be interested in dropping the old index of the dataframe which was added while resetting the index. Though you can do this manually by using function, you can save this time by passing parameter while resetting the index.

Step 1: Create a DataFrame

pandas reset_index

Step 2: Reset the index with drop=True

pandas reset_index

5. How to convert a column to an index?

You can reset the index of your dataframe without removing the default index by following these steps:

Step 1: Create a DataFrame with initial index

pandas reset_index

Step 2: Set the column as Indexusing set_index

pandas reset_index

6. How to reset multi-level index?

pandas reset_index

Here you can see that level maps to multiple rows. This is a multi-level index. shows the details in greater granularity, and they can be very useful when we are dealing with hierarchical data.

If you apply the function to such type of dataframe, by default, all the levels will be merged into the dataframe as columns:

pandas reset_index

Suppose, you want to reset the index at level. To reset such index, you need to provide the parameter to the function.

pandas reset_index

column still remains as index. Because we specified as the level on which we want to reset the index.

7. Reset only one level in multi-level index

Consider our previous dataframe when it was reset at level:

pandas reset_index

You can see that column, on being reset, is placed at the top level(0) by default. You can modify this level by specifying parameter.

It defines the level at which the shifted index column should be placed. Look at an implementation below:

pandas reset_index

8. How to fill void levels?

Continuing the previous example, you can see that as the column level has been lowered (level 1), a void has been created at the level above it:

pandas reset_index

You can fill this level too using parameter that takes in the name for that.

pandas reset_index

9. Practical Tips

function is very useful in cases when you have performed a lot of preprocessing steps with your data such as removing null values rows or filtering data.

These processes may return a different dataframe whose index is not in continuous manner anymore. Let’s try a small example.

pandas reset_index
pandas reset_index

As you can see in the table above, the indexing of rows has changed. Initially it was 0,1,2… but now it has changed to 0,1,5.

In such cases, you can use function to number the rows in the right order.

pandas reset_index

10. Test your knowledge

Q1: The pandas dataframe index is reset as soon as the function is applied to it. True or False?


Answer: False. Because, the output dataframe is just a view of the changes. To apply the changes, we use parameter.

Q2: What is the use of parameter in function?


Answer: It is used to avoid old index being added to pandas dataframe while resetting the index.

Q3: Which parameter is used change the default level of column while resetting multi-level index?


Answer: We use parameter to define the level of column.

Q4: Answer the following questions using the given dataset.

Q4.1: Reset the index at level, and assign an upper level for . Save the output as .



Q4.2: Use the output of Question 1 to add an upper level named for . Make sure that still remains the index



pandas reset_index

Q4.3: Calculate the rank of the students where branch is and sorted in decreasing order of . Print rank and name of student both.



This blog has been contributed by Kaustubh Gupta, under the guidance of ML+ team.

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In this short guide, you’ll see how to reset an index in Pandas DataFrame.

In general, you can reset an index in Pandas DataFrame using this syntax:


Let’s now review the steps to reset your index using a simple example.

Steps to Reset an Index in Pandas DataFrame

Step 1: Gather your data

For illustration purposes, let’s gather the following data about various products:


Step 2: Create a DataFrame

Next, create a DataFrame to capture the above data in Python:

import pandas as pd data = {'Product': ['Tablet', 'Printer', 'Laptop', 'Monitor', 'Computer'], 'Price': [250, 150, 1200, 300, 1500] } df = pd.DataFrame(data, columns= ['Product', 'Price']) print(df)

When you run the code in Python, you’ll get the following DataFrame:

Notice that the index values (highlighted in yellow) are sequential from 0 to 4, such that the:

  • First row (Tablet for a price of 250) has an index of 0
  • Second row (Printer for a price of 150) has an index of 1
  • Third row (Laptop for a price of 1200) has an index of 2
  • Fourth row (Monitor for a price of 300) has an index of 3
  • Fifth row (Computer for a price of 1500) has an index of 4

Step 3: Drop Rows from the DataFrame

Before you reset the index in the DataFrame, let’s create a scenario where the index will no longer be sequential.

One way to do that is by dropping some of the rows from the DataFrame. For example, let’s drop the first row (index of 0), as well as the fourth row (index of 3):

df = df.drop([0, 3])

So the full code would look like this:

import pandas as pd data = {'Product': ['Tablet', 'Printer', 'Laptop', 'Monitor', 'Computer'], 'Price': [250, 150, 1200, 300, 1500] } df = pd.DataFrame(data, columns= ['Product', 'Price']) df = df.drop([0, 3]) print(df)

You’ll now notice that the index is no longer sequential:

Step 4: Reset the Index in Pandas DataFrame

To reset the index in the DataFrame you’ll need to apply the following syntax:


Putting everything together:

import pandas as pd data = {'Product': ['Tablet', 'Printer', 'Laptop', 'Monitor', 'Computer'], 'Price': [250, 150, 1200, 300, 1500] } df = pd.DataFrame(data, columns= ['Product', 'Price']) df = df.drop([0, 3]) df = df.reset_index(drop=True) print(df)

You’ll now get a sequential index that starts from 0:

Categories PythonSours:
What do I need to know about the pandas index? (Part 1)

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0 from dataframe pandas reindex

Sanya completely lay down on the seat, his feet were on the ground, Anka leaned back on him, while I, lifting her legs above the. Roof of the car (she was wearing sneakers and pants with panties lowered to them, so that's the only way), sat down a little and entered cunt. Of course, it was not convenient for me to fuck her like that, so sometimes I went out and got up with bent legs.

Otherwise they were already shaking in the process.

MetPy Mondays #96 - Pandas Multi-Index Dataframes

Finally, he dug into her plump lips, and at the same time inserted his fingers into her vagina and ass. She screamed but her mouth was closed with a kiss. So he kissed her for a while, then moved away and moved closer to the other end of the bed.

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To be continued. the last one along the corridor to the left. In the beginning Toha, Anya, kitchen, bath, push. Only Anya's light was on and soft music was playing in the room, so we decided to make some noise. We went to the open door.

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