Rename panda column

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Rename panda column

How to rename columns in Pandas DataFrameGiven a Pandas DataFrame, let's see how to rename column names.About Pandas DataFrame:Pandas DataFrame are rectangular grids which are used to store data. It is easy to visualize and work with data when stored in dataFrame.It consists of rows and

columns.Each row is a measurement of some instance while column is a vector which contains data for some specific attribute/variable.Each dataframe column has a homogeneous data throughout any specific column but dataframe rows can contain homogeneous or heterogeneous data throughout any

specific row.Unlike two dimensional array, pandas dataframe axes are labeled.Method #1: Using rename() function.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.Rename a single column.import pandas as pdrankings = {'test': ['India', 'South Africa', 'England',

'New Zealand', 'Australia'],

'odi': ['England', 'India', 'New Zealand',

'South Africa',

'Pakistan'],

't20': ['Pakistan', 'India', 'Australia',

'England', 'New Zealand']}rankings_pd = pd.DataFrame(rankings)print(rankings_pd)rankings_pd.rename(columns = {'test':'TEST'}, inplace = True)print("After modifying first column:", rankings_pd.columns)Output:Rename multiple

column.import pandas as pdrankings = {'test': ['India', 'South Africa', 'England',

'New Zealand', 'Australia'],

'odi': ['England', 'India', 'New Zealand',

'South Africa', 'Pakistan'],

't20': ['Pakistan', 'India', 'Australia',

'England', 'New

Zealand']}rankings_pd = pd.DataFrame(rankings)print(rankings_pd.columns)rankings_pd.rename(columns = {'test':'TEST', 'odi':'ODI',

't20':'T20'}, inplace = True)print(rankings_pd.columns)Output: Method #2: By assigning a list of new column namesThe columns can also be renamed by

directly assigning a list containing the new names to the columns attribute of the dataframe object for which we want to rename the columns. The disadvantage with this method is that we need to provide new names for all the columns even if want to rename only some of the columns.import pandas as

pdrankings = {'test': ['India', 'South Africa', 'England',

'New Zealand', 'Australia'],

'odi': ['England', 'India', 'New Zealand',

'South Africa', 'Pakistan'],

't20': ['Pakistan', 'India', 'Australia',

'England', 'New Zealand']}rankings_pd =

pd.DataFrame(rankings)print(rankings_pd.columns)rankings_pd.columns = ['TEST', 'ODI', 'T-20']print(rankings_pd.columns)Output: Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics. To begin with, your interview preparations Enhance your

Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning ? Basic Level Course import pandas as pd a = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6], 'C':[7,8,9]}) print(a) A B C 0 1 4 7 1 2 5 8 2 3 6 9 >>>a.columns = ['a','b','c'] >>>a a b c 0

1 4 7 1 2 5 8 2 3 6 9 >>>a.rename(columns={'A':'a', 'B':'b', 'C':'c'}, inplace = True) >>>a a b c 0 1 4 7 1 2 5 8 2 3 6 9 >>>a.rename(columns={'A':'a', 'C':'c'}, inplace = True) >>>a a B c 0 1 4 7 1 2 5 8 2 3 6 9 datasetDataFrame>>> dataset.columnsIndex(['age', 'job', 'marital',

'education', 'default', 'housing', 'loan','contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays','pre... # -*- coding: UTF-8 -*- import pandas as pd df = pd.DataFrame([{'col1':'a', 'col2':1, 'col3':'aa'}, {'col1':'b', 'col2':2, 'col3':'bb'}, {'col1':'c', 'col2':3, 'col3':'cc'}, {'col1':'a', 'col2':44, 'co ?2020 CSDN :

:CSDN In this Python tutorial you'll learn how to modify the names of columns in a pandas DataFrame. The tutorial consists of two examples for the modification of the column names in a pandas DataFrame. To be more specific, the article will contain this information: Let's

dive into it. Example Data & Add-On Packages To be able to use the functions of the pandas library, we first need to import pandas: import pandas as pd # Load pandasimport pandas as pd # Load pandas Furthermore, consider the following example data: data = pd.DataFrame({"x1":range(7, 1, - 1), #

Create pandas DataFrame "x2":["a", "b", "c", "d", "e", "f"], "x3":["X", "Y", "X", "X", "Y", "X"]}) print(data) # Print pandas DataFramedata = pd.DataFrame({"x1":range(7, 1, - 1), # Create pandas DataFrame "x2":["a", "b", "c", "d", "e", "f"], "x3":["X", "Y", "X", "X", "Y", "X"]}) print(data) # Print pandas DataFrame As

you can see based on Table 1, our example data is a DataFrame composed of six rows and three columns. The variables in our DataFrame are called x1, x2, and x3. Example 1: Change Names of All Variables Using columns Attribute Example 1 explains how to rename the column names of all variables

in a data set. The following Python code uses the columns attribute to create a copy of our DataFrame where the original header is replaced by the new column names col1, col2, and col3. data_new1 = data.copy() # Create copy of DataFrame data_new1.columns = ["col1", "col2", "col3"] # Using columns

attribute print(data_new1) # Print updated pandas DataFramedata_new1 = data.copy() # Create copy of DataFrame data_new1.columns = ["col1", "col2", "col3"] # Using columns attribute print(data_new1) # Print updated pandas DataFrame In Table 2 you can see that we have created a new pandas

DataFrame with updated variables names by running the previous Python programming code. Example 2: Change Names of Specific Variables Using rename() Function The Python programming code below shows how to exchange only some particular column names in a pandas DataFrame. For this,

we can use the rename function as shown below: data_new2 = data.copy() # Create copy of DataFrame data_new2 = data_new2.rename(columns = {"x1": "col1", "x3": "col3"}) # Using rename() print(data_new2) # Print updated pandas DataFramedata_new2 = data.copy() # Create copy of DataFrame

data_new2 = data_new2.rename(columns = {"x1": "col1", "x3": "col3"}) # Using rename() print(data_new2) # Print updated pandas DataFrame As shown in Table 3, we have created another duplicate of our input data matrix, in which we have only renamed the columns x1 and x3. Video & Further

Resources Do you need further information on the contents of this article? Then you could have a look at the following video on the Absent Data YouTube channel. In the video, the speaker illustrates the contents of this tutorial in a live session in Python. In addition, you might read the related Python

programming articles on this website: Introduction to Python Programming To summarize: At this point you should know how to rename the names of variables in a pandas DataFrame in the Python programming language. If you have further questions, please tell me about it in the comments. 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 rename() function to modify specific column names. Use the pandas dataframe set_axis() method to change all your

column names. Set the dataframe's columns 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 ? df.rename(columns={"OldName":"NewName"}) 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 inplace to True. Example 1: Change names of a specific column import pandas as pd # create a dataframe data = {'Category': ['Dog', 'Cat', 'Rabbit', 'Parrot'], 'Color': ['brown', 'black', 'white', 'green']} df = pd.DataFrame(data) # print dataframe columns print("Dataframe columns:",

df.columns) # change column name Category to Pet df = df.rename(columns={"Category":"Pet"}) # print dataframe columns print("Dataframe columns:", df.columns) Output: Dataframe columns: Index(['Category', 'Color'], dtype='object') Dataframe columns: Index(['Pet', 'Color'], dtype='object') In the above

example, the dataframe df is created with columns: Category and Color. The rename() function is then used to change the column name Category to Pet which returns a new dataframe which is saved to df. Example 2: Apply function to column names The rename() function also accepts function that can

be applied to each column name. import pandas as pd # create a dataframe data = {'Col1_Category': ['Dog', 'Cat', 'Rabbit', 'Parrot'], 'Col2_Color': ['brown', 'black', 'white', 'green']} df = pd.DataFrame(data) # print dataframe columns print("Dataframe columns:", df.columns) # change column names to the

string after the _ df = df.rename(columns=lambda x: x.split("_")[1]) # print dataframe columns print("Dataframe columns:", df.columns) Output: Dataframe columns: Index(['Col1_Category', 'Col2_Color'], dtype='object') Dataframe columns: Index(['Category', 'Color'], dtype='object') 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: df.set_axis(new_column_list, axis=1) You have to explicitly specify the axis as

1 or 'columns' to update column names since its default is 0 (which modifies the axis for rows). It returns a new dataframe with the updated axis. To modify the dataframe in-place, set the argument inplace to True. Example: Change column names using set_axis import pandas as pd # create a dataframe

data = {'Category': ['Dog', 'Cat', 'Rabbit', 'Parrot'], 'Color': ['brown', 'black', 'white', 'green']} df = pd.DataFrame(data) # print dataframe columns print("Dataframe columns:", df.columns) # change column name Category to Pet df = df.set_axis(["Pet", "Color"], axis=1) # print dataframe columns

print("Dataframe columns:", df.columns) Output: Dataframe columns: Index(['Category', 'Color'], dtype='object') Dataframe columns: Index(['Pet', 'Color'], dtype='object') In the above example, the set_axis() function is used to rename the column Category to Pet in the dataframe df. 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 columns attribute to your new list of columns. The following is they syntax: df.columns = new_column_list 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. import pandas as pd # create a dataframe data = {'Category': ['Dog', 'Cat', 'Rabbit', 'Parrot'], 'Color': ['brown', 'black', 'white', 'green']} df =

pd.DataFrame(data) # print dataframe columns print("Dataframe columns:", df.columns) # change column name Category to Pet df.columns = ["Pet", "Color"] # print dataframe columns print("Dataframe columns:", df.columns) Output: Dataframe columns: Index(['Category', 'Color'], dtype='object')

Dataframe columns: Index(['Pet', 'Color'], dtype='object') In the above example, we change the column names of the dataframe df by setting df.columns to a new column list. Like the set_index() 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 Subscribe to our newsletter for more such informative guides and tutorials. We do

not spam and you can opt-out any time. DataFrame.rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')[source]? Alter axes labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is.

Extra labels listed don't throw an error. See the user guide for more. Parameters mapperdict-like or functionDict-like or function transformations to apply to that axis' values. Use either mapper and axis to specify the axis to target with mapper, or index and columns. indexdict-like or functionAlternative to

specifying axis (mapper, axis=0 is equivalent to index=mapper). columnsdict-like or functionAlternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper). axis{0 or `index', 1 or `columns'}, default 0Axis to target with mapper. Can be either the axis name (`index', `columns') or number (0,

1). The default is `index'. copybool, default TrueAlso copy underlying data. inplacebool, default FalseWhether to return a new DataFrame. If True then value of copy is ignored. levelint or level name, default NoneIn case of a MultiIndex, only rename labels in the specified level. errors{`ignore', `raise'},

default `ignore'If `raise', raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If `ignore', existing keys will be renamed and extra keys will be ignored. Returns DataFrame or NoneDataFrame with the renamed axis labels or None if

inplace=True. Raises KeyErrorIf any of the labels is not found in the selected axis and "errors='raise'". See also DataFrame.rename_axisSet the name of the axis. Examples DataFrame.rename supports two calling conventions (index=index_mapper, columns=columns_mapper, ...) (mapper, axis={'index',

'columns'}, ...) We highly recommend using keyword arguments to clarify your intent. Rename columns using a mapping: >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) >>> df.rename(columns={"A": "a", "B": "c"}) a c 0 1 4 1 2 5 2 3 6 Rename index using a mapping: >>> df.rename(index={0: "x", 1:

"y", 2: "z"}) A B x 1 4 y 2 5 z 3 6 Cast index labels to a different type: >>> df.index RangeIndex(start=0, stop=3, step=1) >>> df.rename(index=str).index Index(['0', '1', '2'], dtype='object') >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise") Traceback (most recent call last): KeyError: ['C'] not

found in axis Using axis-style parameters: >>> df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6 >>> df.rename({1: 2, 2: 4}, axis='index') A B 0 1 4 2 2 5 4 3 6

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