![]() The column names in this example are obtained using the select() function from the dataframe object. Finally, we use the print function to output the column names. The list of column names is then obtained using the columns attribute, and it is saved in the column_names variable. In this example, we first establish a sample dataframe called df with two columns: "Name" and "Age". Spark = ("Get Column Names").getOrCreate()ĭata = ĭf = spark.createDataFrame(data, ) Using this function, we will obtain a list of every column name that is present in the Dataframe. We are using the columns function to obtain the names of the columns that are present in the Dataframe. The names of the columns can also be obtained from the list of structural fields, which can then be used to retrieve the names of the columns. ![]() Using the columns attribute, a list of strings can be generated as a result of this method's return of a new dataframe containing statistical data about each column. Step5 − An overview of the dataframe's statistics, together with the names of all the columns, can also be obtained using the describe method, which is the last option. This approach makes it simple to determine the names of certain columns by displaying the names and data types of each column in the dataframe. Step4 − The printSchema method in PySpark, which shows the dataframe's schema in a tree-like fashion, is a third methodology for obtaining the name of a column in a dataframe. In order to extract the column name as a string using the columns attribute, this function returns a new dataframe that only contains the selected column. Step3 − Use the select method with the column name as an input to obtain the name of a certain dataframe column in another way. Since no additional calculations or transformations are necessary, this method is straightforward and effective. Step2 − The columns property in PySpark returns a list of all the column names in the dataframe and can be used to retrieve the name of a dataframe column. An individual variable or attribute of the data, such as a person's age, a product's price, or a customer's location, is represented by a column. Step1 − A named collection of data values that are arranged in a tabular fashion constitutes a dataframe column in PySpark. To obtain the name of a dataframe column in PySpark, you should follow the following techniques and steps − Algorithms to get Name of Dataframe Column in PySpark This enables quicker and more effective data analysis because columns in PySpark dataframes are analysed in parallel across multiple nodes. Following the creation of a column, you can use it to carry out a number of operations on the data, including filtering, grouping, and aggregating. This method enables you to name the new column and specify the rules for generating its values. Using the withColumn method, you can add columns to PySpark dataframes. A named collection of data values that are arranged in a tabular fashion constitutes a dataframe column in PySpark.
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