\nTable of Contents:<\/strong><\/p>\n
- \n
- Introduction<\/a><\/li>\n
- Syntax<\/a><\/li>\n
- Examples<\/a><\/li>\n
- Frequently Asked Questions<\/a><\/li>\n<\/ul>\n
Ok. Let’s start with a quick introduction to the rename method.<\/p>\n
<\/a><\/p>\n
A quick introduction to Pandas rename<\/h2>\n
The Pandas rename method is fairly straight-forward: it enables you to rename the columns or rename the row labels of a Python dataframe.<\/p>\n
This technique is most often used to rename the columns of a dataframe (i.e., the variable names).<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2098\/05\/pandas-rename_simple-example.png\")
<\/p>\nBut again, it can also rename the row labels (i.e., the labels in the dataframe index).<\/p>\n
I’ll show you examples of both of these in the examples section<\/a>.<\/p>\n
But first, let’s take a look at the syntax.<\/p>\n
<\/a><\/p>\n
The syntax of Pandas rename<\/h2>\n
Ok, now that I’ve explained what the Pandas rename method does, let’s look at the syntax.<\/p>\n
Here, I’ll show you the syntax for how to rename Pandas columns, and also how to rename Pandas row labels.<\/p>\n
A quick note<\/h4>\n
Everything that I’m about to describe assumes that you’ve imported Pandas and that you already have a Pandas dataframe created.<\/p>\n
You can import pandas with the following code:<\/p>\n
\r\nimport pandas as pd\r\n<\/pre>\n
And if you need a refresher on Pandas dataframes and how to create them, you can read our tutorial on Pandas dataframes<\/a>.<\/p>\n
Syntax to rename Pandas columns<\/h3>\n
Ok, let’s start with the syntax to rename columns. (The syntax for renaming columns and renaming rows labels is almost identical, but let’s just take it one step at a time.)<\/p>\n
When we use the rename method, we actually start with our dataframe<\/em>. You type the name of the dataframe, and then
.rename()<\/code> to call the method.<\/p>\n![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2098\/05\/pandas-rename-columns_syntax-explanation.png\")
<\/p>\nInside the parenthesis, you’ll use the
columns<\/code> parameter, which enables you to specify the columns that you want to change.<\/p>\nHow to use the columns parameter<\/h5>\n
Let’s look carefully at how to use the columns parameter. <\/p>\n
When you change column names using the rename method, you need to present the old column name and new column name inside of a Python dictionary.<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2098\/05\/pandas-rename-columns_syntax-explanation_part2.png\")
<\/p>\nSo if the old variable name is
old_var<\/code> and the new variable name isnew_var<\/code>, you would present to thecolumns<\/code> parameter as key\/value pairs, inside of a dictionary:columns = {'old_var':'new_var'}<\/code>.<\/p>\nIf you have multiple columns that you want to change, you simply provide multiple old name\/new name dictionary pairs, separated by commas.<\/p>\n
To do this properly, you really need to understand Python dictionaries, so if you need a refresher, then read about how dictionaries are structured<\/a>.<\/p>\n
Syntax to rename Pandas row labels<\/h3>\n
Now, let’s look at the syntax for renaming row labels (i.e., the labels in the dataframe index).<\/p>\n
![\"An](\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2098\/05\/pandas-rename-row-labels_syntax-explanation.png\")
<\/p>\nYou’ll notice that the syntax is almost exactly the same as the syntax for changing the column names. <\/p>\n
The major difference is that instead of using the
columns<\/code> parameter, when you want to change the row labels, you need to use theindex<\/code> parameter instead.<\/p>\nBeyond that you’ll still use a dictionary with old name\/new name pairs as the argument to the
index<\/code> parameter.<\/p>\nThe parameters of Pandas rename<\/h3>\n
Now, let’s take a look at the parameters of the rename method.<\/p>\n
The most important parameters that you should know are:<\/p>\n
- \n
columns<\/code><\/li>\nindex<\/code><\/li>\ninplace<\/code><\/li>\n<\/ul>\nThe rename method also has additional parameters \u2013
axis<\/code>,copy<\/code>,levels<\/code>, anderrors<\/code> \u2013 but these are more advanced and less commonly used, so I won’t cover them here.<\/p>\ncolumns<\/code><\/h6>\nThe
columns<\/code> parameter enables you to specify the column names you want to change, and what to change them to.<\/p>\nAs described above, the argument to this parameter can be a dictionary, but it can also be a function.<\/p>\n
When you provide a dictionary, it the values should be structured as old name\/new name pairs, like this:
{'old_var':'new_var'}<\/code>.<\/p>\nindex<\/code><\/h6>\nThe
index<\/code> parameter is very similar to thecolumns<\/code> parameter, except it operates on row index labels instead of column names.<\/p>\nSo the
index<\/code> parameter enables you to specify the row labels you want to change, and what to change them to.<\/p>\nAs described above, the argument to this parameter can be a dictionary, but it can also be a function.<\/p>\n
When you provide a dictionary, it the values should be structured as old name\/new name pairs, like this:
{'old_var':'new_var'}<\/code>.<\/p>\ninplace<\/code><\/h6>\nThe inplace parameter enables you to force the rename method to directly modify the dataframe that’s being operated on.<\/p>\n
By default,
inplace<\/code> is set toinplace = False<\/code>. This causes the rename method to produce a new<\/em> dataframe. In this case, the original dataframe is left unchanged.<\/p>\nIf you set
inplace = True<\/code>, the rename method will directly alter the original dataframe, and overwrite the data directly. Be careful with this, and make sure that your code is doing exactly what you want it to.<\/p>\nThe output of Pandas rename<\/h3>\n
By default, the rename method will output a new<\/em> Python dataframe, with new column names or row labels. As noted above, this means that by default, rename will leave the original dataframe unchanged.<\/p>\n
If you set
inplace = True<\/code>, rename won’t produce any new output. In this case, rename will instead directly modify and overwrite the dataframe that’s being operated on.<\/p>\n<\/a><\/p>\n
Examples: How to rename Pandas columns and Pandas row labels<\/h2>\n
Now that we’ve looked at the syntax, let’s look at some examples.<\/p>\n
Examples:<\/strong><\/p>\n
- \n
- Rename one dataframe column<\/a><\/li>\n
- Rename multiple dataframe columns<\/a><\/li>\n
- Change row labels<\/a><\/li>\n
- Change the column names and row labels ‘in place’<\/a><\/li>\n<\/ul>\n
However, before you run the examples, you’ll need to run some preliminary code to import the modules we need, and to create the dataset we’ll be operating on.<\/p>\n
Import modules<\/h5>\n
First, let’s import Pandas.<\/p>\n
You can do that with the following code:<\/p>\n
\r\n#===============\r\n# IMPORT MODULES\r\n#===============\r\nimport pandas as pd\r\n<\/pre>\n
Notice that we’re importing Pandas with the alias
pd<\/code>. This makes it possible to refer to Pandas aspd<\/code> in our code, which is the common convention among Python data scientists.<\/p>\nCreate DataFrame<\/h5>\n
Next, we’ll create a dataframe that we can operate on.<\/p>\n
We’ll create our dataframe in three steps:<\/p>\n
- \n
- create a Python dictionary<\/li>\n
- create a Pandas dataframe from the dictionary<\/li>\n
- specify the columns and row labels (i.e., the index)<\/li>\n<\/ul>\n
Let’s start by creating a Python dictionary. As you can see, this dictionary contains economic data for several countries.<\/p>\n
\r\n#==========================\r\n# CREATE DICTIONARY OF DATA\r\n#==========================\r\ncountry_data_dict = {\r\n 'country_code':['USA', 'CHN', 'JPN', 'GER', 'UK', 'IND']\r\n ,'country':['USA', 'China', 'Japan', 'Germany', 'UK', 'India']\r\n ,'continent':['North America','Asia','Asia','Europe','Europe','Asia']\r\n ,'gross_domestic_product':[19390604, 12237700, 4872137, 3677439, 2622434, 2597491]\r\n ,'pop':[322179605, 1403500365, 127748513, 81914672, 65788574, 1324171354]\r\n}\r\n<\/pre>\nNext, we’ll create a DataFrame from the dictionary:<\/p>\n
\r\n#=================================\r\n# CREATE DATAFRAME FROM DICTIONARY\r\n#=================================\r\ncountry_data = pd.DataFrame(country_data_dict, columns = ['country_code','country', 'continent', 'gross_domestic_product', 'pop'])\r\n<\/pre>\n
Note that in this step, we’re setting the column names using the
columns<\/code> parameter inside of pd.DataFrame().<\/p>\nFinally, we’ll set the row labels (i.e., the index). By default, Pandas uses a numeric integer index that starts at 0<\/a>.<\/p>\n
We’re going to change that default index to character data.<\/p>\n
Specifically, we’re going to use the values in the
country_code<\/code> variable as our new row labels.<\/p>\nTo do this, we’ll use the Pandas set_index method<\/a>:<\/p>\n
\r\ncountry_data = country_data.set_index('country_code')\r\n<\/pre>\nNotice that we’re assigning the output of
set_index()<\/code> to the original dataframe name,country_data<\/code>, using the equal sign. This is because by default, the output ofset_index()<\/code> is a new dataframe object by default. By default,set_index()<\/code> does not<\/em> modify the DataFrame in place. <\/p>\nOk. Now that we have our dataframe, let’s print it out to look at the contents:<\/p>\n
\r\nprint(country_data)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n country continent gross_domestic_product pop\r\ncountry_code \r\nUSA USA North America 19390604 322179605\r\nCHN China Asia 12237700 1403500365\r\nJPN Japan Asia 4872137 127748513\r\nGER Germany Europe 3677439 81914672\r\nUK UK Europe 2622434 65788574\r\nIND India Asia 2597491 1324171354\r\n<\/pre>\n
This dataframe has economic data for six countries, organized in a row-and-column structure.<\/p>\n
The dataframe has four columns:
country<\/code>,continent<\/code>,gross_domestic_product <\/code>, andpop<\/code>.<\/p>\nAdditionally, notice that the “
country_code<\/code>” variable is set aside off to the left. That’s because we’ve set thecountry_code<\/code> column as the index. The values ofcountry_code<\/code> will now function as the row labels of the dataframe.<\/p>\nNow that we have this dataframe, we’ll be able to use the
rename()<\/code> method to rename the columns and the row labels.<\/p>\n<\/a><\/p>\n
EXAMPLE 1: Rename one dataframe column<\/h3>\n
First, let’s start simple.<\/p>\n
Here, we’re going to rename a single column name.<\/p>\n
Specifically, we’re going to rename the
gross_domestic_product<\/code> variable toGDP<\/code>.<\/p>\nLet’s run the code, and then I’ll explain.<\/p>\n
\r\ncountry_data.rename(columns = {'gross_domestic_product':'GDP'})\r\n<\/pre>\nOUT:<\/p>\n
\r\n country continent GDP pop\r\ncountry_code \r\nUSA USA North America 19390604 322179605\r\nCHN China Asia 12237700 1403500365\r\nJPN Japan Asia 4872137 127748513\r\nGER Germany Europe 3677439 81914672\r\nUK UK Europe 2622434 65788574\r\nIND India Asia 2597491 1324171354\r\n<\/pre>\n
Explanation<\/h5>\n
Notice in the output that
gross_domestic_product<\/code> has been renamed toGDP<\/code>.<\/p>\nHow did we do it?<\/p>\n
We typed the name of the dataframe,
country_data<\/code>, and then used so-called “dot syntax” to call therename()<\/code> method.<\/p>\nInside the parenthesis, we’re using the
columns<\/code> parameter to specify the columns we want to rename, and the new name that we want to use. This ‘old name’\/’new name’ pair is presented as a dictionary<\/em> in the form{'old name':'new name'}<\/code>.<\/p>\nSo we have the synax
columns = {'gross_domestic_product':'GDP'}<\/code>, which is basically saying change the column name'gross_domestic_product'<\/code> to'GDP'<\/code>.<\/p>\nRemember: the original dataframe is unchanged<\/h6>\n
One more thing to point out here: when we run this code, the original dataframe will remain unchanged<\/em>.<\/p>\n
That’s because by default, the Pandas rename method produces a new dataframe as an output and leaves the original unchanged. And by default, this output will be sent directly to the console. So when we run our code like this, we’ll see the new dataframe with the new name in the console, but the original dataframe will be left the same.<\/p>\n
If you want to save the output, you can use the assignment operator like this:<\/p>\n
\r\ncountry_data_new = country_data.rename(columns = {'gross_domestic_product':'GDP'})\r\n<\/pre>\nHere, I’ve given the output dataframe the new name
country_data_new<\/code>.<\/p>\nWe can call it anything we like. We could even call it
country_data<\/code>. Just be careful if you docountry_data_new = country_data.rename(...)<\/code>, it will overwrite your original dataset. Make sure your code works perfectly before you do this!<\/p>\n<\/a><\/p>\n
EXAMPLE 2: Rename multiple columns<\/h3>\n
Next, let’s make things a little more complicated.<\/p>\n
Here, we’ll rename multiple columns at the same time.<\/p>\n
The way to do this is very similar to the code in example 1<\/a>, except here, we’ll provide more old name\/new name pairs in our dictionary.<\/p>\n
Specifically, we’ll rename
gross_domestic_product<\/code> toGDP<\/code>, and we’ll renamepop<\/code> topopulation<\/code>.<\/p>\nLet’s take a look.<\/p>\n
\r\ncountry_data.rename(columns = {'gross_domestic_product':'GDP', 'pop': 'population'})\r\n<\/pre>\nOUT:<\/p>\n
\r\n country continent GDP population\r\ncountry_code \r\nUSA USA North America 19390604 322179605\r\nCHN China Asia 12237700 1403500365\r\nJPN Japan Asia 4872137 127748513\r\nGER Germany Europe 3677439 81914672\r\nUK UK Europe 2622434 65788574\r\nIND India Asia 2597491 1324171354\r\n<\/pre>\n
Explanation<\/h5>\n
This should make sense if you understood example 1.<\/p>\n
Here, we’re calling the
rename()<\/code> method using dot syntax.<\/p>\nInside the parenthesis, we have the code
columns = {'gross_domestic_product':'GDP', 'pop': 'population'}<\/code>.<\/p>\nLook inside the dictionary (i.e., inside the curly brackets). Here, we have two<\/em> old name\/new name pairs. These are organized as key\/value pairs, just as we normally have inside of a dictionary.<\/p>\n
This should be simple to understand, as long as you understand how dictionaries are structured. If you don’t make sure to review Python dictionaries<\/a>.<\/p>\n
<\/a><\/p>\n
EXAMPLE 3: Rename row labels<\/h3>\n
Now, let’s rename some of the row labels.<\/p>\n
Specifically, we’re going to rename the labels
GER<\/code> andUK<\/code>.<\/p>\nTo do this, we’re going to use the
index<\/code> parameter. <\/p>\nLet’s take a look.<\/p>\n
\r\ncountry_data.rename(index = {'GER':'DEU','UK':'GBR'})\r\n<\/pre>\nOUT:<\/p>\n
\r\n country continent gross_domestic_product pop\r\ncountry_code \r\nUSA USA North America 19390604 322179605\r\nCHN China Asia 12237700 1403500365\r\nJPN Japan Asia 4872137 127748513\r\nDEU Germany Europe 3677439 81914672\r\nGBR UK Europe 2622434 65788574\r\nIND India Asia 2597491 1324171354\r\n<\/pre>\n
Explanation<\/h5>\n
Here, we’re renaming
GER<\/code> toDEU<\/code> and we’re renamingUK<\/code> toGBR<\/code>.<\/p>\nTo do this, we called the rename method and used the code
index = {'GER':'DEU','UK':'GBR'}<\/code> inside the parenthesis. <\/p>\nThe
index<\/code> parameter enables us to specify the row labels that we want to change. And we’re using the dictionary as the argument, which contains the old value\/new value pairs.<\/p>\n<\/a><\/p>\n
EXAMPLE 4: Change the column names and row labels ‘in place’<\/h3>\n
Finally, let’s change some of the columns and row labels ‘in place’.<\/p>\n
As I mentioned in example 1<\/a>, and in the syntax section<\/a>, by default, the rename method leaves the original dataframe unchanged. That’s because by default, the
inplace<\/code> parameter is set toinplace = False<\/code>. This causes the rename method to produce a new dataframe as the output, while leaving the original dataframe unchanged.<\/p>\nBut sometimes, we actually want to modify the original dataframe directly.<\/p>\n
To do this we can set
inplace = True<\/code>.<\/p>\nCreate copy of dataframe<\/h5>\n
Before we run our code, we’re actually going to make a copy of our data.<\/p>\n
The reason is that we’re going to directly overwrite a dataframe. This can be dangerous if you get it wrong, so we’re actually going to work with a copy of the original.<\/p>\n
\r\ncountry_data_copy = country_data.copy()\r\n<\/pre>\n
Now, we have a dataframe,
country_data_copy<\/code>, which contains the same data as the original.<\/p>\nRename columns and labels in place<\/h5>\n
Now, we’re going to directly rename the columns and row labels of
country_data_copy<\/code>.<\/p>\nTo do this, we’ll use rename with the
inplace<\/code> parameter as follows:<\/p>\n\r\ncountry_data_copy.rename(index = {'GER':'DEU','UK':'GBR'}\r\n ,columns = {'gross_domestic_product':'GDP', 'pop': 'population'}\r\n ,inplace = True\r\n )\r\n<\/pre>\nAnd now, let’s print out the data, so we can see it.<\/p>\n
\r\nprint(country_data_copy)\r\n<\/pre>\n
OUT:<\/p>\n
\r\n country continent GDP population\r\ncountry_code \r\nUSA USA North America 19390604 322179605\r\nCHN China Asia 12237700 1403500365\r\nJPN Japan Asia 4872137 127748513\r\nDEU Germany Europe 3677439 81914672\r\nGBR UK Europe 2622434 65788574\r\nIND India Asia 2597491 1324171354\r\n<\/pre>\n
Explanation<\/h5>\n
As you can see in the output the row labels and column names have been changed directly in
country_data_copy<\/code>.<\/p>\nWe simply did this by setting
inplace = True<\/code>.<\/p>\nAgain: be careful with this. When you do this, you’ll directly modify and overwrite your data. Check your code and double check again to make sure that your code works correctly before using
inplace = True<\/code>.<\/p>\n<\/a><\/p>\n
Frequently asked questions about Pandas rename<\/h2>\n
Let’s quickly cover a common question about the Pandas rename technique.<\/p>\n
Frequently asked questions:<\/strong><\/p>\n
- \n
- Why is my dataframe unchanged after I use the rename function?<\/a><\/li>\n<\/ul>\n
- Rename multiple dataframe columns<\/a><\/li>\n
- Syntax<\/a><\/li>\n