Pandas DataFrame | lt method
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Pandas DataFrame.lt(~) method returns a DataFrame of booleans where True indicates an entry that is strictly less than the specified value.
Parameters
1. otherlink | scalar or sequence or Series or DataFrame
The value(s) to compare with.
2. axislink | int or string | optional
Whether to perform the comparison along the columns or the rows:
Axis | Description |
|---|---|
| Compare each column. |
| Compare each row. |
By default, axis="columns".
3. level | int or string | optional
The levels to perform comparison on. This is only relevant if your source DataFrame is a multi-index.
Return Value
A DataFrame of booleans.
Examples
Consider the following DataFrame:
df = pd.DataFrame({"A":[3,4],"B":[5,6]})df
A B0 3 51 4 6
Passing in a acalar
To check for values strictly less than 5 in the DataFrame:
df.lt(5)
A B0 True False1 True False
Comparing rows
By default, axis=1, which means that passing in a sequence will result in a comparison with each row:
df.lt([4,5]) # axis=1
A B0 True False1 False False
Here, we are comparing each row of the source DataFrame with [4,5]. This means that we are performing the following pair-wise comparisons:
(row one) [3,5] < [4,5] = [True, False](row two) [4,6] < [4,5] = [False, False]
We show df here for your reference:
df
A B0 3 51 4 6
Comparing columns
By setting axis=0, we can compare each column with the specified sequence:
df.lt([4,5], axis=0)
A B0 True False1 True False
Here, we're performing the following pair-wise comparisons:
(column A) [3,4] < [4,5] = [True, True](column B) [5,6] < [4,5] = [False, False]
Case with missing values
Any comparison with missing values will result in False for that entry.
Consider the following DataFrame with a missing value:
df = pd.DataFrame({"A":[3,pd.np.nan],"B":[5,6]})df
A B0 3.0 51 NaN 6
Performing a comparison yields:
df.lt(5)
A B0 True False1 False False
Notice how NaN < 5 returned False.
