Pandas DataFrame | dropna method
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Pandas DataFrame.dropna(~) method removes rows or columns with missing values.
Parameters
1. axislink | int or string | optional
Whether or not to remove rows or columns with missing values:
Axis | Description |
|---|---|
| Scans through each row, and if a missing value exists, drop the row. |
| Scans through each column, and if a missing value exists, drop the column. |
By default, axis=0.
2. howlink | string | optional
The criteria by which to remove a row/column:
How | Description |
|---|---|
| If the row or column consists of at least one missing value, then remove it. |
| If the row or column consists of all missing values, then remove it. |
By default, how="any".
3. thresh | int | optional
The number of non-NaN a row/column must at least contain to not be dropped. For instance, if thresh=2, then
a column with 1 non-missing value will be dropped.
a column with 2 non-missing values will be kept.
a column with 3 non-missing values will be kept.
By default, no minimum is set.
4. subsetlink | array-like of strings | optional
The columns to check for missing values when scans are performed row-wise (when axis=0). By default, all columns are considered. Consult examples below for clarification.
5. inplacelink | boolean | optional
If
True, then the method will directly modify the source DataFrame instead of creating a new DataFrame.If
False, then a new DataFrame will be created and returned.
By default, inplace=False.
Return Value
A DataFrame with rows or columns that contain missing values removed according to the provided parameters.
Examples
Consider the following DataFrame:
df
A B C0 NaN 3 51 2.0 4 6
Removing rows with missing values
To remove rows with missing value(s):
A B C1 2.0 4 6
Notice how the first row (i.e. index=0) was removed since it contained a missing value.
Removing columns with missing values
To remove columns with missing value(s):
B C0 3 51 4 6
Notice how column A was removed since it contained a missing value.
Removing column with ALL missing values
Consider the following DataFrame:
df
A B C0 NaN 3 NaN1 2.0 4 NaN
To remove columns whose values are all missing values, set how="all":
A B0 NaN 31 2.0 4
Notice how only column C was removed as it contained only missing values.
Setting a threshold
Consider the following DataFrame:
A B0 a 3.01 NaN 4.02 NaN NaN
To remove columns with at least 2 non-NaN values, set thresh=2:
B0 3.01 4.02 NaN
Notice how column A, which only had one non-missing value, was removed, while column B with 2 non-missing values was kept.
Removing rows with missing values for certain columns only
Consider the following DataFrame:
df
A B C0 NaN 3 NaN1 2.0 4 NaN
To remove rows where the value corresponding to column A is missing:
A B C1 2.0 4 NaN
Notice how only the first row (index=0) was removed despite the fact that both the two rows contained missing values. This is because, by specifying subset=["A"], the method only checks for missing values in column A.
Removing columns with missing values for certain rows only
Consider the following DataFrame:
df
A B C0 NaN 3 51 2.0 4 6
To remove columns where the value corresponding to row index 1 is missing:
A B0 NaN 31 2.0 4
Notice how only column C was removed, despite the fact that column A also contained a missing value. This is because by specifying subset=[1], the method will only check for missing values at row index=1 (i.e. the second row). Since the value corresponding to column C in row index=1 was a missing value, the method removed column C.
Removing rows/columns in-place
To drop row(s) or column(s) in-place, we need to set inplace=True. This will directly modify the source DataFrame instead of creating and returning a new DataFrame.
As an example, consider the following DataFrame:
df
A B C0 NaN 3 51 2.0 4 6
We remove all rows containing missing value(s) with inplace=True:
df
A B C1 2.0 4 6
As shown in the output, the source DataFrame has been modified.
