I saw this code in someone's iPython notebook, and I'm very confused as to how this code works. As far as I understood, pd.loc[] is used as a location based indexer where the format is:
df.loc[index,column_name]
However, in this case, the first index seems to be a series of boolean values. Could someone please explain to me how this selection works. I tried to read through the documentation but I couldn't figure out an explanation. Thanks!
iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'
Whenever slicing (a:n
) can be used, it can be replaced by fancy indexing (e.g. [a,b,c,...,n]
). Fancy indexing is nothing more than listing explicitly all the index values instead of specifying only the limits.
Whenever fancy indexing can be used, it can be replaced by a list of Boolean values (a mask) the same size than the index. The value will be True
for index values that would have been included in the fancy index, and False
for the values that would have been excluded. It's another way of listing some index values, but which can be easily automated in NumPy and Pandas, e.g by a logical comparison (like in your case).
The second replacement possibility is the one used in your example. In:
iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'
the mask
iris_data['class'] == 'versicolor'
is a replacement for a long and silly fancy index which would be list of row numbers where class
column (a Series) has the value versicolor
.
Whether a Boolean mask appears within a .iloc
or .loc
(e.g. df.loc[mask]
) indexer or directly as the index (e.g. df[mask]
) depends on wether a slice is allowed as a direct index. Such cases are shown in the following indexer cheat-sheet:
It's pandas
label-based selection, as explained here: https://pandas.pydata.org/pandas-docs/stable/indexing.html#selection-by-label
The boolean array is basically a selection method using a mask.
This is using dataframes from the pandas
package. The "index" part can be either a single index, a list of indices, or a list of booleans. This can be read about in the documentation: https://pandas.pydata.org/pandas-docs/stable/indexing.html
So the index
part specifies a subset of the rows to pull out, and the (optional) column_name
specifies the column you want to work with from that subset of the dataframe. So if you want to update the 'class' column but only in rows where the class is currently set as 'versicolor', you might do something like what you list in the question:
iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'
It's a pandas data-frame and it's using label base selection tool with df.loc
and in it, there are two inputs, one for the row and the other one for the column, so in the row input it's selecting all those row values where the value saved in the column class
is versicolor
, and in the column input it's selecting the column with label class
, and assigning Iris-versicolor
value to them.
So basically it's replacing all the cells of column class
with value versicolor
with Iris-versicolor
.
Source: Stackoverflow.com