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# Updating rows based on column values in Pandas DataFrame

schedule Aug 12, 2023
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# Filling rows where condition is based on their values with a constant

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]}, index=["a","b","c"])df A B Ca 1 4 7b 2 5 8c 3 6 9 ```

To fill rows where value for column `A` is `1` or value for column `C` is greater than or equal to `9`:

``` df.loc[(df["A"] == 1) | (df["C"] >= 9)] = 0df A B Ca 0 0 0b 2 5 8c 0 0 0 ```

Here, we are first extracting the following `Series` of booleans:

``` (df["A"] == 1) | (df["C"] >= 9) a Trueb Falsec Truedtype: bool ```

Passing in this boolean mask into the `loc` property will return the rows that correspond to `True`. We then fill these rows with the value `0` using standard assignment (`=`).

# Filling certain row values where condition is based on their values with a constant

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]}, index=["a","b","c"])df A B Ca 1 4 7b 2 5 8c 3 6 9 ```

Instead of filling the entire rows with a constant, you can specify which rows to fill like so:

``` df.loc[(df["A"] == 1) | (df["C"] >= 9), "B"] = 0df A B Ca 1 0 7b 2 5 8c 3 0 9 ```

Here, the `"B"` after the comma indicates that we want to only update column `B`, and leave the other column values intact.

# Filling rows where condition is based on a function of their values

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]}, index= ["a","b","c"])df A B Ca 1 4 7b 2 5 8c 3 6 9 ```

To fill rows where the sum of the value for column `A` and the value for column `B` is greater than `6`:

``` def criteria(my_df): return my_df["A"] + my_df["B"] > 6df.loc[criteria] = 0df A B Ca 1 4 7b 0 0 0c 0 0 0 ```

To clarify, `criteria(my_df)` takes in as argument the source DataFrame, and returns a `Series` of booleans where `True` corresponds to the rows that satisfy the condition:

``` def criteria(df): print(df["A"] + df["B"] > 6) return df["A"] + df["B"] > 6df.loc[criteria] = 0 a Falseb Truec Truedtype: bool ```

The `loc` property will then return all the rows that correspond to `True` in this boolean mask.

# Filling rows using a function of their values

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]}, index= ["a","b","c"])df A B Ca 1 4 7b 2 5 8c 3 6 9 ```

To double the values of rows where the value for column `B` is larger than `4`:

``` df.loc[df["B"] > 4] = df * 2df A B Ca 1 4 7b 4 10 16c 6 12 18 ```

Here, `loc` returns all the rows where the value for column `B` is larger than `4`. These rows are then assigned new values using `=`. Note that the assignment only updates the rows returned by `loc`, and so the rows that do not satisfy the condition will be kept intact.

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