Lets say I have the following dataframe data = [['Tom', 180], ['Adam', 174], ['Bob', 182]] df = pd.DataFrame(data, columns=['Name', 'Height']) If I wish to change the value of Bob's height, I could use .at as follows: df.at[2, 'Height'] = 190. However since every name is unique, Im looking for a way to do something along the line of df.at['Bob', 'Height'] = 190. After tinkering with I bit, I ended up opting to first find the location of Bob within df and then use its location along side .at location = np.where(df['Name'].values == 'Bob')[0][0] df.at[location, 'Height'] = 190 However, There must be a better way to do this. Note that I am not adamant on the use of .at property. I think my solution is reasonable, but I want to know if there is something better out there. After playing with it some more I found this df.loc[df['Name'] == 'Bob', 'Height'] = 190 I am quite happy with this. Clearly this method would cause issues if I had repeats. But fortunately I do not. Continue reading...
