can not understand labeled concept in loc indexer in pandas
Question:
I have a question and want to get an understanding of it, so the question is in pandas we have two indexers (loc, iloc) that we can use to select specific columns and rows, and the loc is labeled based on data selecting method which means that we have to pass the name of the row or column which we want to select, and iloc is an indexed based selecting method which means that we have to pass integer index in the method to select a specific row/column, so if I have the below data frame with columns names as a "string"
I can use the loc as below:
so if I rename the column to a number and the type of name of the column becomes ‘integer’ as below:
and using loc if I provide the name of the column that types integer why does this work without errors:
I mean if loc for labeled data why it works on column names as numbers 0, 1, 2, 3, with type "INTEGER"?
Answers:
You misunderstand the definition of label. That doesn’t mean it’s a string but rather how it’s displayed on the screen. The problem is that we often (always) use names for columns and numbers for rows.
sr = pd.Series([7, 12, 49], index=[11, 12, 13])
print(sr)
# Output
11 7
12 12
13 49
dtype: int64
The first index of this series is 14 if you use .loc (by label) or 0 if you use .iloc (by position) but unfortunately the automatic RangeIndex of Pandas start to 0 so using .loc and .iloc looks like the same (but not!)
df = pd.DataFrame(np.arange(1, 10).reshape(3, 3), index=list('XYZ'), columns=range(101, 104))
print(df)
101 102 103
X 1 2 3
Y 4 5 6
Z 7 8 9
To get the value 5, you can use:
# Remember, this is what you see to the screen (this is the label)
>>> df.loc['Y', 102]
5
# I have to think to convert what you see to coordinates (this is the position)
>>> df.iloc[1, 1]
5
I have a question and want to get an understanding of it, so the question is in pandas we have two indexers (loc, iloc) that we can use to select specific columns and rows, and the loc is labeled based on data selecting method which means that we have to pass the name of the row or column which we want to select, and iloc is an indexed based selecting method which means that we have to pass integer index in the method to select a specific row/column, so if I have the below data frame with columns names as a "string"
I can use the loc as below:
so if I rename the column to a number and the type of name of the column becomes ‘integer’ as below:
and using loc if I provide the name of the column that types integer why does this work without errors:
I mean if loc for labeled data why it works on column names as numbers 0, 1, 2, 3, with type "INTEGER"?
You misunderstand the definition of label. That doesn’t mean it’s a string but rather how it’s displayed on the screen. The problem is that we often (always) use names for columns and numbers for rows.
sr = pd.Series([7, 12, 49], index=[11, 12, 13])
print(sr)
# Output
11 7
12 12
13 49
dtype: int64
The first index of this series is 14 if you use .loc (by label) or 0 if you use .iloc (by position) but unfortunately the automatic RangeIndex of Pandas start to 0 so using .loc and .iloc looks like the same (but not!)
df = pd.DataFrame(np.arange(1, 10).reshape(3, 3), index=list('XYZ'), columns=range(101, 104))
print(df)
101 102 103
X 1 2 3
Y 4 5 6
Z 7 8 9
To get the value 5, you can use:
# Remember, this is what you see to the screen (this is the label)
>>> df.loc['Y', 102]
5
# I have to think to convert what you see to coordinates (this is the position)
>>> df.iloc[1, 1]
5