Python rename inplace

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Python rename inplace

The Pandas Python library is an extremely powerful tool for graphics, tracking and data analysis. However, the power (and therefore the complexity) of the pandas can often be rather overwhelming, given the myriad of functions, methods and capacity of the library. In this short tutorial, we will explore the basic use of the Dataframe in Pandas, which is the structure of the basic data for the entire system and how to use the index and column labels to keep track of data within the Dataframe. Create a basic datograph for this tutorial, we need something to work with, so we create a very simple frame of data that consists of 3 titles of books and author names: PD.DafaFrame ([("JRR Tolkien", "" JRR Tolkien '), (' Robinson Crusoe ',' Daniel Dephoe '), (' Moby-Dick ',' Herman Melville ')]) Note: Throughout the tutorial the examples will include a great amount of excess spacing. This spacing It is not required, but it is used to better illustrate the syntax we use. The result of the creating dataframe described above is a simple 3-line column table, with automatically generated numeric indexes and columns: ?, 0 1 0 lo Hobbit JRR Tolkien 1 Robinson Crusoe Daniel Defoe 2 Moby-Dick Herman Melville Adding columns and indexes When creating a DataFrame, it is quite possible to specify the column and index labels. To do this, you need to specify the values For data parameters, the index e of the columns: pd.daframe (date = [('the hobbit', 'jrr tolkien'), ('robinson crusoe', 'daniel defoe') ('moby-dick', 'herman melville')], columns = [ 'Title', 'Author'], Index = ['First', 'Second', 'Third']) ?, author of the first JRR title. Tolkien Second Robinson Crusoe Daniel Defoe Third Moby-Dick Herman Melville Now let's see our data structure has some appropriate indexing and column labels that make a little meaning. However, what happens when we have an existing dateframe and we want to update the column labels on the fly? Changing column labels There are two methods to change column labels: the column method and the renamed method. Using the column method if we have our dataframe labeled already created, the simpler method for overwriting column labels is to call the column method on the dataframe object and provide the new list of names that we like to specify. For example, if we take our original datafame: df = pd.dafaframe (["hobbit", "jrr tolkien"), ("robinson crusoe", "daniel dofoe"), ("moby-dick", "herman melville ' )]) DF ? ? 0 1 0 The Hobbit JRR Tolkien 1 Robinson Crusoe Daniel Defoe 2 Moby-Dick Herman Melville We can change column labels by adding the following line: df.columns = ["title", "Author"] DF ? , Author Title 0 The Hobbit JRR Tolkien 1 Robinson Crusoe Daniel Defoe 2 Moby-Dick Herman Melville Using the method Rename the other technique for renaming the column labels is to call the Rename method on the DataFrame object, then passing our list of label values on the column parameter: df = pd. Dataframe ([('The Hobbit', 'JRR Tolkien'), ('Robinson Crusoe', 'Daniel Defoe'), ('Moby-Dick', 'Herman Melville ')]) df.rame (column = {0:' title ', 1:' author '}, inplace = true) df | | title | author | | ? ? ?,? "? ? ?,?" | ? ? ?,? "? ? ?,?" | ? ? ?,? "? ? ?,?" | | 0 | Hobbit | J.R.R. Tolkien |. | 1 | Robinson Crusoe | Daniel Defoe | | 2 | Moby-Dick | Herman Melville | It is important to note that since the renamed method is trying to actually rename the existing labels, it is necessary to specify the existing label first followed by the new label to rename it later, as shown in the example reported above. Furthermore, we specify the true value for the parameter spoken here because we want to update the dataframe instead of having this call function returns instead a newly created datalogom. You will learn how to rename column labels in Pandas. This is very common when you work with data structures like Dataframes. How can you rename columns in a Datafame Pandas? The Rename Pandas DataFrame function allows you to rename labels labels Columns in a DataFrame using a dictionary that specifies the current and new values of the labels. There are several ways to rename the columns with the function of renaming (eg using dictionaries, normal functions or Lambdas). We will see some examples that show how to rename the columns of a Pandas DataFrame. In the end of this tutorial, this will be very clear. To do this we can use a python dictionary after importing the panda module: import panda as pd df = pd.dafaframe ({? ? ? ? ? ? ? ? ? ? ? ? ", United Kingdom", ? ?Germany?, ? ? Greece? ?], ? Capitals? ?: [? ROME? ??, London ??, Berlin? ??, Athens? ?]}) Print (DF) Here is the dataframe we created: Capital Cities 0 Italy Rome 1 Kingdom United London 2 Germany Berlin 3 Greece Ateneun Dataframe can also be created by the CSV format using the Read_CSV function. To rename the columns of a panda dataFrame we can use the Rename function and pass a dictionary. The dictionary contains the names of the current columns as keys and new column names as values.df.name (columns = {? ? ? ? ? ? ? ? ? ? ? ? ? ?Capital?: ? ?Capital?}) After performing this Command We get the following: ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ? , ?, ?, ?, ?, ?, ?, ITALY ?, ?, rome 1?, United Kingdom?, London 2 ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ? ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, is, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, new dataframe: new_df = df.name (columns = {? ?Netherlands? : ? ?Country?, ? ?Capital?: ? ?capital?}) Print (new_df) [output] ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ? ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, united kingdom? , London 2 ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, germany?, Berlin 3?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, can use the head function that Return the first N Rig He of the Dataframe: Print (New_Df.head (1)) [Output] Capital Countries 0 Italy Roma Powder You can see, the HEAD function prints the column header (which contains column labels) and the first line of the DataFrame. A previous section DataFrame column We have seen how to name all the columns of a dataFrame by assigning the output of the Rename function to a new DataFrame.Con Pandas We also have the possibility to update the DataFrame columns in place, that is we can update The original dataFrame instead of creating a new one. To update the DataFrame columns on the spot using the Pandas Rename function we need to set the Inplace topic to True.df. Remember (Columns = {? ? ? ? ? " Capital ?: ? Capital ?}, Inplace = True) Print (DF) [output] Capital country 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Ateneil Parameter Inplace is a boolean whose default value is false.Inlre If Inplace is true The Renominame Function returns no one: >>>> Print (df. Remember (Columns = {? ? ? ? ? ? ? ? ? "Capitali?: ? ?Capital?}, Inplace = True)) No periods, now you know Two ways to update the columns of the DataFrame columns. The column into a Pandaspandas dataframe also allows you to update a column in a DataFrame DataFrame. We see how? ? ?|Df. Relame (Columns = {? ?Country?: ? ?Country? ?}, Inplace = True) Print (DF) [output] Capital country 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Attites We updated the name of the first column simply by including the name of the first column in the dictionary in the same way we can update only The second column of our dataframe. And now let's see what happens if we try to switch to the Renominame function a dictionary that contains a column name that does not exist. DF. INPLACE = TRUE) PRINT (DF) The Rename function updates the name of the Basandos columns I in the dictionary passed to it only if there is a specific column name in the otherwise it has no effect (unless the error parameter is set to "raise"). In this scenario, we see what happens if we pass an additional parameter called errors and set its value for "raise": df.rename(columns={"Population":"POPULATION"}, inplace=True, error="raise")Pandas raises the following exception KeyError to tell us that there is no column called "Population": KeyError default axis Therefore we did not see errors when the error parameter was not present in our expression. Rename a column in Pandas by location Can you rename a column in a dataframe based on your index? Yeah, that's how... First we introduce the attribute of columns that returns the names of columns of a DataFrame.print (df.columns) [output] Index(['COUNTRY', 'Capital'], dtype='object) We can access the variable returned by the attribute of columns as a list and use it to rename a specific column. For example, to rename the last column we can use: df.rename(columns={ df.columns[-1]: "CAPITAL" }, inplace = True) print(df) [output] CAPITAL OF PAESE 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece AthensRemember that you can access the last element of a list using index -1. Rename DataFrame Columns with a ListSimilarly, you can also assign new column values to the attribute of DataFrame:df.columns = ['CoUnTrIeS','CaPiTaLs'] print(df) [output] CoUnTrIeS CaPiTaLs 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece I usually prefer to use the same way of rename columns for consistency. My favorite way is to pass a dictionary to the rename function. Rename a column in Pandas Using a common FunctionA scenario is trying to rename the columns in a DataFrame by tiny or uppercase. To do this we can use standard Python functions along with the rename dataframe.df.rename(columns=str.lower, inplace=True) print(df) [output] countries capitals 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Athens For example, here we used the lowest string method to transform column labels into tiny strings. What other string methods could you use? How to apply a Lambda to Rename DataFrame function In the previous section we saw how to apply a function to the columns of a dataframe. Considering that the lambdas are functions (to be precise anonymous functions) we can also apply them to change the value of the columns. Here is how... df.rename(columns=lambda x: x[:2], inplace=True) print(df) [output] co ca 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Athens As you can see, we are using the following lamb function...lambda x: x[:2]...to set the column name value to their first two characters. Rename Index for a Pandas DataFrame We used the rename function to rename the columns in a DataFrame. The same can be done for the index. For example, we start from the following dataframe: Capital Countries 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Athens I want to replace 0.1.2,3 with Nation 0, Nation 1, etc... With the following call to the replacement function I can rename the index: df.rename(index={0:"Nation 0", 1: "Nation 1", 2: "Nation 2", 3: "Nation 3"}, inplace=True) print(df) [output] Countries Capitals Nation 0 Italy Rome Nation 1 United Kingdom London Nation 2 Germany Berlin Nation 3 Greece Athens To update the index of a DataFrame pass a dictionary to the index parameter of the rename function. The dictionary keys represent the current index and dictionary values the new index. I could also use a lamb to avoid passing that long dictionary: (index=lambda x: "Nation" + str (x), inplace=True) print (df) Can you see how to reduce duplication using a lamb? Before continuing, try the expression above and confirm that The result is correct.axis used during renaming of columns or index The Rename function can also be called using a different convention. This convention uses the AXIS parameter to be sent if the Rename function indicates index or columns. Here are the possible values for Axis: the index is intended for use of ? ? ~Index?TM or 0 as axis value (this is the default value) .Columns is intended for use of ? ~ Columns?TM or 1 as a value of Axis.Below you can view the generic syntax: dataframe.rename (Mapper, axis = {'index', 'columns'}) The Mapper can be a dictionary or function that transforms the values of a specific axis. For example, we allow how to rewrite calls to the rename function used before in this tutorial? | all expressions update columns in the same way way way waydf.rename (columns = {"Country": "Paese", "Capitali" : "Capitale"}, inplase = true) df.rename ({"Paesi": "Paese," "Capitals." "Capital"}, Axis = 'column', INPOSTANTE = TRUE) DF.Rename ({"Country" : "Country", "Capitals": "Capital"}, Axis = 1, inplace = true) 2. Rename Indexall Expressions Update the index in the same way: DF.rename (index = Lambda x: "nation" + str (x), inplace = true) df.rename (lambda x: nation "+ str (x), axis = 'Index', INPROCE = TRUE) DF.RENAME (LAMBDA X: "NATION" + STR (X), AXIS = 0, INPIARE = TRUE) makes sense? Make sure the two conventions are the same column. And index at the same time of the end now we have seen how to rename the columns or index, but we can also rename it with a single expression. It is an example that updates both columns and index: df.rename (columns = {"Country" : "Country", "Capitals": "Capital"}, index = Lambda X: "nation" + str (x), inplace = true) You can see that we have passed both parameters columns and index to the Rename function. The columns with add_prefix and add_suffixpandas make available two more functions to rename the columns in a dataframe: add_prefix: adds a prefix to all column names.add_suffix: adds a suffix to all column names. They only return a DATAFRAME with the updated columns. Let's see how they work in practice... "Let's start with the following dataframe: Capitals 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Athens Add_prefix to DateFrame to be added ? ~ coll_TM before each column Label: print (DF.ADD_PREFIX ('COL_') [Uscita] Col_DF Output Col_capitals 0 Italy Rome 1 United Kingdom London 2 Germany Berlin 3 Greece Athensand in a similar way for ADD_SUFF Now you know how to rename columns in a dataframe using Pandas. You actually have more ways to do it depending on what you want. And you know how to rename a dataframe index too. We also saw how to combine DataFrame rename function with other Python functions, including Lambdas. The source code for this tutorial so you can download it and try it on your machine. Once, well done! I'm Ma Tech Lead, Softwareer Engineer and Coach Programming. I want to help you on your journey to become a super developer! Share knowledge with your friends! Friends!

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