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'
pd.DataFrame.loc
can take one or two indexers. For the rest of the post, I'll represent the first indexer as i
and the second indexer as j
.
If only one indexer is provided, it applies to the index of the dataframe and the missing indexer is assumed to represent all columns. So the following two examples are equivalent.
df.loc[i]
df.loc[i, :]
Where :
is used to represent all columns.
If both indexers are present, i
references index values and j
references column values.
Now we can focus on what types of values i
and j
can assume. Let's use the following dataframe df
as our example:
df = pd.DataFrame([[1, 2], [3, 4]], index=['A', 'B'], columns=['X', 'Y'])
loc
has been written such that i
and j
can be
scalars that should be values in the respective index objects
df.loc['A', 'Y']
2
arrays whose elements are also members of the respective index object (notice that the order of the array I pass to loc
is respected
df.loc[['B', 'A'], 'X']
B 3
A 1
Name: X, dtype: int64
Notice the dimensionality of the return object when passing arrays. i
is an array as it was above, loc
returns an object in which an index with those values is returned. In this case, because j
was a scalar, loc
returned a pd.Series
object. We could've manipulated this to return a dataframe if we passed an array for i
and j
, and the array could've have just been a single value'd array.
df.loc[['B', 'A'], ['X']]
X
B 3
A 1
boolean arrays whose elements are True
or False
and whose length matches the length of the respective index. In this case, loc
simply grabs the rows (or columns) in which the boolean array is True
.
df.loc[[True, False], ['X']]
X
A 1
In addition to what indexers you can pass to loc
, it also enables you to make assignments. Now we can break down the line of code you provided.
iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'
iris_data['class'] == 'versicolor'
returns a boolean array.class
is a scalar that represents a value in the columns object.iris_data.loc[iris_data['class'] == 'versicolor', 'class']
returns a pd.Series
object consisting of the 'class'
column for all rows where 'class'
is 'versicolor'
When used with an assignment operator:
iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'
We assign 'Iris-versicolor'
for all elements in column 'class'
where 'class'
was 'versicolor'