Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).
Now let’s see how to get the specified row value of a given DataFrame.
We shall be using loc[ ], iloc[ ], and [ ] for a data frame object to select rows and columns from our data frame.
- iloc[ ] is used to select rows/ columns by their corresponding labels.
- loc[ ] is used to select rows/columns by their indices.
- [ ] is used to select columns by their respective names.
Method 1: Using iloc[ ].
Example: Suppose you have a pandas dataframe and you want to select a specific row given its index.
Python3
# import pandas libraryimport pandas as pd # Creating a dictionaryd = {'sample_col1': [1, 2, 3], 'sample_col2': [4, 5, 6], 'sample_col3': [7, 8, 9]} # Creating a Dataframedf = pd.DataFrame(d) # show the dataframe print(df) print() # Select Row No. 2print(df.iloc[2]) |
Output:
Method 2: Using loc[ ].
Example: Suppose you want to select rows where the value of a given column is given.
Python3
# import pandas libraryimport pandas as pd # Creating a dictionaryd = {'sample_col1': [1, 2, 1], 'sample_col2': [4, 5, 6], 'sample_col3': [7, 8, 9]} # Creating a Dataframedf = pd.DataFrame(d) # show the dataframeprint(df) print() # Select rows where sample_col1 is 1print(df.loc[df['sample_col1'] == 1]) |
Output:
Method 3: Using [ ] and iloc[ ].
Example: Suppose you want only the values pertaining to specific columns of a specific row.
Python3
# import pandas libraryimport pandas as pd # Creating a dictionaryd = {'sample_col1': [1, 2, 1], 'sample_col2': [4, 5, 6], 'sample_col3': [7, 8, 9]} # Creating a Dataframedf = pd.DataFrame(d) # show the dataframeprint(df) print() # Display column 1 and 3 for row 2print(df[['sample_col1' , 'sample_col3']].iloc[1]) |
Output:
