Rename header pandas

Rename header pandas DEFAULT

Renaming column names in Pandas

I'll focus on two things:

  1. OP clearly states

    I have the edited column names stored it in a list, but I don't know how to replace the column names.

    I do not want to solve the problem of how to replace or strip the first character off of each column header. OP has already done this step. Instead I want to focus on replacing the existing object with a new one given a list of replacement column names.

  2. where is the list of new columns names is as simple as it gets. The drawback of this approach is that it requires editing the existing dataframe's attribute and it isn't done inline. I'll show a few ways to perform this via pipelining without editing the existing dataframe.


Setup 1
To focus on the need to rename of replace column names with a pre-existing list, I'll create a new sample dataframe with initial column names and unrelated new column names.


Solution 1

It has been said already that if you had a dictionary mapping the old column names to new column names, you could use .

However, you can easily create that dictionary and include it in the call to . The following takes advantage of the fact that when iterating over , we iterate over each column name.

This works great if your original column names are unique. But if they are not, then this breaks down.


Setup 2
Non-unique columns


Solution 2
using the argument

First, notice what happens when we attempt to use solution 1:

We didn't map the list as the column names. We ended up repeating . Instead, we can use the argument of the function while iterating through the columns of .


Solution 3
Reconstruct. This should only be used if you have a single for all columns. Otherwise, you'll end up with for all columns and converting them back requires more dictionary work.

Single

Mixed


Solution 4
This is a gimmicky trick with and . allows us to set an index inline, but there is no corresponding . So we can transpose, then , and transpose back. However, the same single versus mixed caveat from solution 3 applies here.

Single

Mixed


Solution 5
Use a in that cycles through each element of .
In this solution, we pass a lambda that takes but then ignores it. It also takes a but doesn't expect it. Instead, an iterator is given as a default value and I can then use that to cycle through one at a time without regard to what the value of is.

And as pointed out to me by the folks in sopython chat, if I add a in between and , I can protect my variable. Though, in this context I don't believe it needs protecting. It is still worth mentioning.

Sours: https://stackoverflow.com/questions/11346283/renaming-column-names-in-pandas

Data Independent Data Independent

Pandas Change Column names – Changing column names within pandas is easy. You only need to decide which method you want to use. Depending on your use case, you can pick the best one for you.

The easiest and most popular one will be done via the .rename() method. But look below for 2 other ways.

pandas.DataFrame.rename(columns={'old_column_name':'new_column_name'})

I use this function when I want to clean up my column names. It’s good to practice table hygiene and keep your column names short and readable.

Pseudo Code: Rename your old column name to your new column name.

Pandas Change Column Names

Pandas Change Column Names - How to change your column names in pandas with 3 methods

Method 1 – Pandas Rename

The first method that we suggest is using Pandas Rename. Rename takes a dict with a key of your old column name and a key of your new column name. Amazingly, it also takes a function!

This means that you’re able to apply a string function to your column names and apply a transformation to all of your column names. For instance if you wanted to upper case your column names.

pandas.DataFrame.rename(columns={"old_column_name" : "new_column_name"})

Method 2 – Pandas .columns attribute

The second way to rename your columns is by setting DataFrame.columns to the list of your new column names. This will completely overwrite your original columns with your new list.

Watch out though – Pandas does not know which columns you’re trying to rename. If you mistakenly mis-order your new column names you will have column names that don’t match the column contents. See below for an example

pandas.DataFrame.columns = ['your', 'new', 'column', 'names']

Method 3 – Pandas .set_axis(axis=1)

The last method (and our least favorite) is to set_axis on top of your DataFrame and specify axis=1. This will have similar functionality as setting .columns. In this case, Pandas will completely overwrite all of your column names with whatever you give it.

pandas.DataFrame.set_axis(['your', 'new', 'column', 'names'2], axis=1)

In order to grok these methods, let’s look at a couple of examples

Pandas Change Column Names¶

Let's change the names of our DataFrame's columns. We will run through 3 methods

  1. Method 1 - change column names via .rename()
  2. Method 1 - change column names via .rename() using function mapper
  3. Method 2 - change column names via .columns()
  4. Method 3 - change column names via set_axis()
df=pd.DataFrame([('Foreign Cinema','Restaurant',289.0),('Liho Liho','Restaurant',224.0),('500 Club','bar',80.5),('The Square','bar',25.30)],columns=('name','type','AvgBill'))df
nametypeAvgBill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3

1. Method 1 - change column names via .rename()¶

The most straight forward and explicit way to change your column names is via .rename(). I like this method the most because you can easily change one, or all of your column names via a dict.

Here I'm going to change the column name 'AvgBill' to 'Bill'. You need ot tell pandas that you want to change your columns so you'll need to specify axis=1 or columns=your_mapper. I marginally prefer setting 'columns=' because I don't need to remember if columns is axis=1 or 0.

A mapper is a fancy word for 'What do you want to convert your old values into?'

df.rename({'AvgBill':'Bill'},axis=1)
nametypeBill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3
df.rename(columns={'AvgBill':'Bill'})
nametypeBill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3

2. Method 1 - change column names via .rename() using function mapper¶

Another beautiful thing you can do with rename is pass a function as a mapper. Pandas will apply the function to the column names.

In this case I'm going to upper each of my column names.

Then I'm going to remove the first letter from each of my column names.

df.rename(columns=str.upper)
NAMETYPEAVGBILL
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3
df.rename(columns=lambdax:x[1:])
ameypevgBill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3

3. Method 2 - change column names via .columns()¶

The next way to change your column names is by setting them all specifically via a list. This is slightly more verbose because you need to outline all of your column names, not just the ones you want to change.

Also, you're at a higher risk of mislabeling column names if you accidentally get your orders confused. Be careful with this one.

df.columns=['Name','type','ABill']df
NametypeABill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3
df.columns=['Bill','name','type']df
Billnametype
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3

4. Method 3 - change column names via set_axis()¶

Lastly, you could also change your column names by setting your axis. This last method is great, but doesn't have many advantages (if any) over the first two.

df.set_axis(["Name","Type","AvgBill"],axis=1)
NameTypeAvgBill
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3
df.set_axis(["Name","Type","Bills"],axis=1)
NameTypeBills
0Foreign CinemaRestaurant289.0
1Liho LihoRestaurant224.0
2500 Clubbar80.5
3The Squarebar25.3

Link to code above

Check out more Pandas functions on our Pandas Page

Official Documentation

Sours: https://www.dataindependent.com/pandas/pandas-change-column-names/
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note.nkmk.me

You can rename (change) column / index names (labels) of by using , , , or updating the / attributes.

The same methods can be used to rename the label (index) of .

This article describes the following contents with sample code.

  • Rename column / index name (label):
    • Change multiple names (labels)
    • Update the original object:
    • Rename with functions or lambda expressions
  • Add prefix / suffix to columns: ,
  • Rename all names (labels)
    • Update the / attributes of
  • For
    • ,
    • Update the attributes of

method that sets an existing column as an index is also provided. See the following article for detail.

As an example, create as follows:

source: pandas_dataframe_rename.py

Rename column / index name (label)): rename()

You can use the method of to change column / index name individually.

Specify the original name and the new name in like to / argument of .

is for the columns name and is for index name. If you want to change either, you can only specify one of or .

A new is returned, the original is not changed.

source: pandas_dataframe_rename.py

Change multiple names (labels)

You can change multiple names at once by adding elements to .

source: pandas_dataframe_rename.py

Update the original object: inplace

By default the original is not changed, and a new is returned.

Setting the parameter to changes the original . In this case, no new is returned, and the return value is .

source: pandas_dataframe_rename.py

Rename with functions or lambda expressions

Functions (callable objects) can also be specified in the parameter and of the method.

Applying a function to convert upper and lower case:

source: pandas_dataframe_rename.py

It is also possible to apply lambda expressions.

source: pandas_dataframe_rename.py

Add prefix / suffix to columns: add_prefix(), add_suffix()

and that add prefixes and suffixes to columns names are provided.

The strings specified in the argument is added to the beginning or the end of columns names.

source: pandas_dataframe_rename.py

and only rename . If you want to add prefixes or suffixes to , specify the lambda expression in the argument with the method as described above.

Also, and do not have . If you want to update the original object, overwrite it like .

Rename all names (labels)

To change all names, use the method or update / attributes.

set_axis()

You can change all column / index names by method of .

Specify new column / index names as the first parameter in a list-like object such as or .

Setting the parameter to or updates , and setting it to or updates . If omitted, will be updated.

source: pandas_dataframe_rename.py

Note that an error raises if the size (number of elements) of the list specified in the first parameter does not match the number of rows and columns.

source: pandas_dataframe_rename.py

By default the original is not changed, and a new is returned. Setting the parameter to changes the original .

source: pandas_dataframe_rename.py

Update the columns / index attributes of pandas.DataFrame

You can also directly update the and attributes of .

Lists and tuples can be assigned to the and attributes.

source: pandas_dataframe_rename.py

Note that an error raises if the size (number of elements) of the list does not match the number of rows and columns.

source: pandas_dataframe_rename.py

For pandas.Series

You can change the label name () of as shown in previous examples of .

As an example, create as follows:

source: pandas_series_rename.py

pandas.Series.rename()

source: pandas_series_rename.py

pandas.Series.add_prefix(), pandas.Series.add_suffix()

source: pandas_series_rename.py

pandas.Series.set_axis()

Update the index attributes of pandas.Series

Sours: https://note.nkmk.me/en/python-pandas-dataframe-rename/
How do I rename columns in a pandas DataFrame?

How to rename column header of a Pandas DataFrame?

Recipe Objective

Have you ever tried to rename a column in pandas dataframe by header. That is setting first element of column as the name of the column.

So this is the recipe on how we can rename column header of a Pandas DataFrame.

Step 1 - Import the library

We have imported pandas which will be needed for the dataset.

Step 2 - Setting up the Data

We have created a dataframe with features as "0", "1", "2" and "3".

 

Step 3 - Renaming column with header

So Here first we have selected the first element of the columns and stored it in header and then we have selected the rest of the elements of the dataframe. Now we are renaming the columns with the values we have stored in the header. So the output comes as:

0 1 2 3 0 first_name last_name age Rating_Score 1 Penny Jacobson 52 29 2 Tina Raj 38 51 3 Jake Milner 27 25 4 Amy Cooze 12 13 0 first_name 1 last_name 2 age 3 Rating_Score Name: 0, dtype: object 0 1 2 3 1 Penny Jacobson 52 29 2 Tina Raj 38 51 3 Jake Milner 27 25 4 Amy Cooze 12 13 first_name last_name age Rating_Score 1 Penny Jacobson 52 29 2 Tina Raj 38 51 3 Jake Milner 27 25 4 Amy Cooze 12 13

Sours: https://www.projectpro.io/recipes/rename-column-header-of-pandas-dataframe

Header pandas rename

While working with data it may happen that you require to change the names of some or all the columns of a dataframe. Whether you’re changing them to correct a typo or simply to give columns more readable names, it’s quite handy to know how to quickly rename columns. In this tutorial, we’ll cover some of the different ways in pandas to rename column names along with examples.

How to rename columns in pandas?

To rename columns of a dataframe you can –

  • Use the pandas dataframe function to modify specific column names.
  • Use the pandas dataframe method to change all your column names.
  • Set the dataframe’s attribute to your new list of column names.

Using pandas rename() function

The pandas dataframe rename() function is a quite versatile function used not only to rename column names but also row indices. The good thing about this function is that you can rename specific columns. The syntax to change column names using the rename function is –

The rename() function returns a new dataframe with renamed axis labels (i.e. the renamed columns or rows depending on usage). To modify the dataframe in-place set the argument to .

Example 1: Change names of a specific column

Output:

In the above example, the dataframe is created with columns: and . The function is then used to change the column name to which returns a new dataframe which is saved to .

Example 2: Apply function to column names

The function also accepts function that can be applied to each column name.

Output:

In the above example, we pass a function to the rename function to modify the column names. The function gets applied to each column and gives its respective new name. Here, we split the column name on and use the second string as our new column.

Using pandas set_axis() function

The pandas dataframe set_axis() method can be used to rename a dataframe’s columns by passing a list of all columns with their new names. Note that the length of this list must be equal to the number of columns in the dataframe. The following is the syntax:

You have to explicitly specify the axis as or to update column names since its default is (which modifies the axis for rows). It returns a new dataframe with the updated axis. To modify the dataframe in-place, set the argument to .

Example: Change column names using set_axis

Output:

In the above example, the function is used to rename the column to in the dataframe . Note that we had to provide the list of all columns for the dataframe even if we had to change just one column name.

Changing the columns attribute

You can also update a dataframe’s column by setting its attribute to your new list of columns. The following is they syntax:

Note that new_column_list must be of same length as the number of columns in your dataframe.

Example: Change column name by updating the columns attribute.

Output:

In the above example, we change the column names of the dataframe by setting to a new column list. Like the function, we had to provide the list of all columns for the dataframe even if we had to change just one column name.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

More on Pandas DataFrames –


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Tags:columnsdataframepandaspython

Sours: https://datascienceparichay.com/article/pandas-rename-column-names/
37- Pandas DataFrames: Renaming the Index

Pandas Tutorial: Renaming Columns in Pandas Dataframe

In this Pandas tutorial, we will go through how to rename columns in a Pandas dataframe. First, we will learn how to rename a single column. Second, we will go on with renaming multiple columns. In the third example, we will also have a quick look at how to rename grouped columns. Finally, we will change the column names to lowercase.

First, however, we are goign to look at a simple code example on how to rename a column in a Pandas dataframe.

How to Rename a Column in Pandas Dataframe

First of all, renaming columns in Pandas dataframe is very simple: to rename a column in a dataframe we can use the rename method:

Code language:Python(python)

In the code example above, the column “OldName” will be renamed “NewName”. Furthermore, the use of the inplace parameter make this change permanent to the dataframe.

Why Bother with Renaming Variables?

Now, when we are working with a dataset, whether it is big data or a smaller data set, the columns may have a name that needs to be changed. For instance, if we have scraped our data from HTML tables using Pandas read_html the column names may not be suitable for our displaying our data, later. Furthermore, this is at many times part of the pre-processing of our data.

Prerequisites

Now, before we go on and learning how to rename columns, we need to have Python 3.x and Pandas installed. Now, Python and Pandas can be installed by installing a scientific Python distribution, such as Anaconda or ActivePython. On the other hand, Pandas can be installed, as many Python packages, using Pip: pip install pandas. Refer to the blog post about installing Python packages for more information.

If we install Pandas, and we get a message that there is a newer version of Pip,we can upgrade pip using pip, conda, or Anaconda navigator.

How to Rename Columns in Pandas?

renaming columns in pandas

So how do you change column names in Pandas? Well, after importing the data, we can change the column names in the Pandas dataframe by either using or assigning a list the columns method; .

Example Data

In this tutorial, we are going to read an Excel file with Pandas to import data. More information about importing data from Excel files can be found in the Pandas read excel tutorial, previously posted on this blog.

Code language:Python(python)

Now, we used the index_col argument, because the first column in the Excel file we imported is the index column. If we want to get the column names from the Pandas dataframe we can use df.columns:

Code language:Python(python)

Now, it is also possible that our data is stored in other formats such as CSV, SPSS, Stata, or SAS. Make sure to check out the post on how to use Pandas read_csv to learn more about importing data from .csv files.

How to Rename a Single Column in Pandas

In the first example, we will learn how to rename a single column in Pandas dataframe. Note, in the code snippet below we use df.rename to change the name of the column “Subject ID” and we use the inplace=True to get the change permanent.

Code language:Python(python)

In the next section, we will have a look at how to use Pandas’ rename function to rename multiple columns.

How To Rename Columns in Pandas: Example 1

To rename columns in Pandas dataframe we do as follows:

  1. Get the column names by using df.columns (if we don’t know the names)
  2. Use the df.rename, use a dictionary of the columns we want to rename as input.

Here’s a working example on renaming columns in Pandas:

Code language:Python(python)

Renaming Columns in Pandas Example 2

Another example of how to rename many columns in Pandas dataframe is to assign a list of new column names to df.columns:

Code language:Python(python)

As can be seen in the code above, we imported our data from an Excel file, we created a list with the new column names. Finally, we renamed the columns with df.columns.

Renaming Columns while Importing Data

In this section, we are going to learn how to rename columns while reading the Excel file. Now, this is also very simple. To accomplish this we create a list before we use the read_excel method. Note, we need to add a column name, in the list, for the index column.

Code language:Python(python)

Importantly, when changing the name of the columns while reading the data we need to know the number of columns before we load the data.

In the next example, we are going to learn how to rename grouped columns in Pandas dataframe.

Renaming Grouped Columns in Pandas Dataframe

In this section, we are going to rename grouped columns in Pandas dataframe. First, we are going to use Pandas groupby method (if needed, check the post about Pandas groupby method for more information). Second, we are going rename the grouped columns using Python list comprehension and df.columns, among other dataframe methods.

Code language:Python(python)

Now, as you can see in the image above, we have a dataframe with multiple indexes. In the next code chunk, however, we are going to rename the grouped dataframe.

Code language:Python(python)

Note, in the code above we also used Pandas ravel method to flatten the output to an ndarray. Now, there are other ways we can change the name of columns in Pandas dataframe. For instance, if we only want to change the column names so that the names are in lower case we can use str.lower.

Code language:Python(python)

Importantly, if we want the change to be permanent we need to add the inplace=True argument.

Changing Column Names in Pandas Dataframe to Lowercase

To change all column names to lowercase we can use the following code:

Code language:Python(python)

Now, this is one way to preprocess data in Python with pandas. In another post, on this blog, we can learn about data cleaning in Python with Pandas and Pyjanitor.

Video Guide: Pandas Rename Column(s)

If you prefer to learn audiovisually, here’s a YouTube Tutorial covering how to change the variable names in Pandas dataframe.

Conclusion

In this post, we have learned all we need to know about renaming columns in Pandas dataframes. First, we learned how to rename a single column. Subsequently, we renamed many columns using two methods; df.rename and df.columns. Third, we renamed the columns while importing the data from an Excel file. In the third example, we learned how to rename grouped dataframes in Pandas. Finally, we also changed the column names to lowercase.

Sours: https://www.marsja.se/renaming-columns-in-pandas-dataframe/

Similar news:

Renaming columns in a Pandas DataFrame

In data analysis, we may work on a dataset that has no column names or column names contain some unwanted characters (e.g. space), or maybe we just want to rename columns to have better names. These all require us to rename columns in a Pandas DataFrame.

In this article, you’ll learn 5 different approaches to do that. This article is structured as follows:

  1. Passing a list of names to attribute
  2. Using function
  3. Renaming columns while reading a CSV file
  4. Using method
  5. Renaming columns via

For demonstration, we will be using a subset from the Titanic dataset available on Kaggle.

def load_data():
df_all = pd.read_csv('data/titanic/train.csv')
# Take a subset
return df_all.loc[:, ['PassengerId', 'Pclass', 'Name', 'Sex']]df = load_data()

Please check out Notebook for the source code.

Pandas DataFrame provides a attribute for us to access the column names. The column name can also be renamed by directly passing a list of new names to the attribute.

# Access column names
>>> df.columns
Index(['PassengerId', 'Pclass', 'Name', 'Sex'], dtype='object')# Rename PassengerId to Id, Pclass to Class
>>> df.columns = ['Id', 'Class', 'Name', 'Sex']

However, a disadvantage with this approach is that we need to provide names for all columns even if we want to rename only some of them. Otherwise, we will get a “ValueError: Length mismatch”:

Pandas has a built-in function called to change the column names. It’s useful when you want to rename some selected columns.

2.1 Rename columns using a dictionary mapping

To rename columns, we can pass a dictionary to the argument. The keys are the columns you want to change and the values are the new names for these columns. We can also set the argument to for the change to happen in the existing DataFrame.

# Rename columns
# PassengerId -> Id
# Pclass -> Classdf.rename(
columns=({ 'PassengerId': 'Id', 'Pclass': 'Class'}),
inplace=True,
)df.head()

2.2 Rename columns using a function

Instead of a dictionary mapping, we can also pass a function to the argument. For example, to convert column names into lowercase, we can pass a function:

df.rename(columns=str.lower).head()

We can also create a custom function and pass it to the argument.

def toUpperCase(string):
return string.upper()
df.rename(columns=toUpperCase).head()

We can also use lambda expression:

df.rename(columns=lambda s: s.upper()).head()

This is useful when you need to update many columns or all columns with the same naming convention.

2.3 Rename index

Turns out can be used to rename index as well. Let’s first create a DataFrame:

df = pd.DataFrame(
{ "ID": [1, 2], "City": ['London', 'Oxford']},
index=['A1', 'A2'],
)

We can use dictionary mapping to rename index:

df.rename({ 'A1': 'B1', 'A2': 'B2' })

We can also pass a function and set the argument to or

df.rename(str.lower, axis=0)

We can actually rename the columns while reading a CSV file. Here is what we can do to override the column names:

new_names = ['ID', 'Survived', 'Class', 'Name', 'Sex']df = pd.read_csv(
'data/titanic/train.csv',
names=new_names, # Rename columns
header=0, # Drop the existing header row
usecols=[0,1,2,3,4], # Read the first 5 columns
)

We created a list of column names and pass it to the argument. The CSV file already contains a header row, therefore the argument is needed to override the column names.

If you want to learn more about , please check out the below article.

There is a method called [1] allow us to replaces a specified phrase with another specified phrase, for example, replace space with an underscore.

df = pd.DataFrame(
{ "account id": [1, 2], "uk city": ['London', 'Oxford']},
index=['A1', 'A2'],
)df.columns = df.columns.str.replace(' ', '_')

This method is also available to index via .

Lastly, we could also change column names by setting the axis. This method is great, but it doesn’t many advantages over the first two.

df.set_axis(['ID', 'Survived', 'Class', 'Name', 'Sex'], axis=1)

This article shows 5 different approaches to rename columns in Pandas DataFrame. Here is a summary:

  • the attribute is the easiest but it requires us to provide names for all columns even if we want to rename only some of them. This approach is sensible when working on a DataFrame with just a couple of columns.
  • When reading a CSV file, it may be more sensible to rename columns using with argument.
  • When you want to rename some selected columns, the function is the best choice.
  • is useful only when you want to replace characters. Note, passing a custom function to can do the same.
  • Lastly, we could also change column names by setting axis via .

I hope this article will help you to save time in learning Pandas. I recommend you to check out the Pandas documentation for those APIs and to know about other things you can do.

Thanks for reading. Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning.

You may be interested in some of my other Pandas articles:

More tutorials can be found on my Github

Reference

Sours: https://towardsdatascience.com/renaming-columns-in-a-pandas-dataframe-1d909360ddc6


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