To select a particular number of rows and columns, you can do the following using. To replicate the above DataFrame, pass the column names as a list to the. Select all the rows, and 4th, 5th and 7th column: The rows and column values may be scalar values, lists, slice objects or boolean. iloc and loc indexers to select rows and columns simultaneously. You have to pass parameters for both row and column inside the. Selecting Rows and Columns Simultaneously Here, I am selecting the rows between the indexes 0.9970 and 0.9959. You can perform the same thing using loc. The above operation selects rows 2, 3 and 4. This is similar to slicing a list in Python. You can use slicing to select multiple rows. iloc indexer to reproduce the above DataFrame. To select rows with different index positions, I pass a list to the. To select the third row in wine_df DataFrame, I pass number 2 to the. iloc and loc for selecting rows from our DataFrame. To illustrate this concept better, I remove all the duplicate rows from the "density" column and change the index of wine_df DataFrame to 'density'. Now, the wine_df_2 DataFrame has the columns in the order that I wanted. Then, I pass the new_cols variable to the indexing operator and store the resulting DataFrame in a variable "wine_df_2" . I use the Set module to check if new_cols contains all the columns from the original. I organize the names of my columns into three list variables, and concatenate all these variables to get the final column order. Wine_df.columns shows all the column names. I would like to change the order of my columns. Then, I pass the regex parameter to the filter method to find all the columns that has a number. Here, I first rename the ph and quality columns. You can use regular expressions with the regex parameter in the filter method. In the above example, the filter method returns columns that contain the exact string 'acid'. The like parameter takes a string as an input and returns columns that has the string. You can also use the filter method to select columns based on the column names or index labels. The list values can be a string or a Python object. To select only the float columns, use wine_df.select_dtypes(include = ). The select_dtypes method takes in a list of datatypes in its include parameter. In this example, there are 11 columns that are float and one column that is an integer. To select columns using select_dtypes method, you should first find out the number of columns for each data types. Selecting columns using "select_dtypes" and "filter" methods
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