Getting specific values from a Pandas DataFrame using multiple conditions with df.loc

This particular section in the 1.1.4 version documentation is giving me trouble

In [45]: df1
Out[45]: 
          A         B         C         D
a  0.132003 -0.827317 -0.076467 -1.187678
b  1.130127 -1.436737 -1.413681  1.607920
c  1.024180  0.569605  0.875906 -2.211372
d  0.974466 -2.006747 -0.410001 -0.078638
e  0.545952 -1.219217 -1.226825  0.769804
f -1.281247 -0.727707 -0.121306 -0.097883

It then proceeds to perform slicing based on labels

In [50]: df1.loc[:, df1.loc['a'] > 0]
Out[50]: 
          A
a  0.132003
b  1.130127
c  1.024180
d  0.974466
e  0.545952
f -1.281247

However, it appears that the value at index 'f' is negative.

Considering the condition is on index 'a', the outcome of the entire first column seems perplexing.

pythonpandas

Answer №1

First, let's examine the specific code snippet below. When you execute,

df.loc['a']>0

This selects all the values of index 'a' that meet this condition. In this instance, only the value of column A corresponding to index 'a' is greater than 0.

Output:

A     True
B    False
C    False
D    False
Name: a, dtype: bool

Now with the following command df.loc[:,df.loc['a']>0, you are essentially performing the following:

df.loc['a':'f',[True,False,False,False]]

Here, you are requesting to select the first column of the df for all indexes. Since you are specifically asking for just column 'A' in your df, it displays:

    A
a   0.132003
b   1.130127
c   1.024180
d   0.974466
e   0.545952
f   -1.281247

Refer to the pandas loc documentation for more information on how df.loc operates.

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