Selecting Data Cookbook

Accessing a single value of a DataFrame Accessing columns of a DataFrame using column labels Accessing columns of a DataFrame using integer indices Accessing rows of a DataFrame using integer indices Accessing rows of a DataFrame using row labels Accessing the first n rows Accessing the last n rows Accessing values of a multi-index DataFrame Adding prefix to column labels Adding suffix to column labels Converting two columns into a dictionary Excluding columns based on type Getting earliest or latest date from DataFrame Getting every nth row Getting indexes of rows matching conditions Getting shortest and longest strings Selecting a single column as a DataFrame Selecting columns of a DataFrame using regex Selecting last column of DataFrame Selecting rows based on a condition Extracting values of a DataFrame as a Numpy array Getting a list of all the column labels Getting all columns except one Getting all duplicate rows Getting all numeric columns of a DataFrame Getting all unique values of columns Getting column label of max value in each row Getting column label of minimum value in each row Getting columns by data type Getting columns using integer index Getting first row value of a column Getting index of Series where value is True Getting integer index of a column using its column label Getting integer index of rows based on column values Getting multiple columns Getting row with largest index value Getting row with smallest index value Getting rows based on multiple column values Getting rows except some Getting rows from a DataFrame based on column values Getting rows that are not in other DataFrame Getting rows using OR statement Getting rows where column values are of specific length Getting rows where value is between two values Getting rows where values do not contain substring Getting the column labels of a DataFrame Getting the first column Getting the index of a DataFrame Getting the length of the longest string in a column Getting the longest string in a column Getting the row with the maximum column value Getting the row with the minimum column value Getting the shape of a DataFrame Getting number of columns of a DataFrame Getting the total number of rows of a DataFrame Getting the total number of values in a DataFrame Making column labels all lowercase Making column labels all uppercase Randomly select rows based on a condition Randomly selecting n columns from a DataFrame Randomly selecting n rows from a DataFrame Reassigning column values Retrieving DataFrame column values as a NumPy array Selecting columns that do not begin with certain prefix Selecting columns with certain prefix Selecting n rows with the smallest values for a column Selecting rows based on dates Selecting rows from a DataFrame whose column values are contained in a list Selecting rows from a DataFrame whose column values are NOT contained in a list Selecting rows from a DataFrame whose column values contain a substring Selecting top n rows with the largest values for a column Splitting DataFrame based on column values

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Accessing columns of a DataFrame using column labels in Pandas

Programming

Python

Pandas

Cookbooks

DataFrame Cookbooks

Selecting Data Cookbook

Jul 1, 2022

Last updated

local_offer Python●Pandas

Example

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[3,4],"B":[5,6]}, index=["a","b"])
df
                
            
               A  B
a  3  5
b  4  6

Accessing a single column

To access a single column:


        
        
            
                
                
                    df["A"]
                
            
            a    3
b    4
Name: A, dtype: int64

We could also use the loc property, which is slightly more verbose:


        
        
            
                
                
                    df.loc[:,"A"]
                
            
            a    3
b    4
Name: A, dtype: int64

Here, the : before the comma indicates that we want to retrieve all rows. The "A" after the comma then indicates that we just want to fetch column A.

Accessing multiple columns

Consider the same df as above:


        
        
            
                
                
                    df = pd.DataFrame({"A":[3,4],"B":[5,6]}, index=["a","b"])
df
                
            
               A  B
a  3  5
b  4  6

To access multiple columns, pass in a list of column labels:


        
        
            
                
                
                    df[["A","B"]]
                
            
               A  B
a  3  5
b  4  6

Using loc property:


        
        
            
                
                
                    df.loc[:,["A","B"]]
                
            
               A  B
a  3  5
b  4  6

The only difference with the single-case is that here we pass in a list of column labels as opposed to a single string.

Pandas DataFrame | loc property

Access and update values of the DataFrame using row and column labels.

Accessing columns of a DataFrame using integer indices in Pandas

To access columns of a DataFrame using integer indices in Pandas, use the DataFrame.iloc property.