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# Pandas DataFrame | eq method

*schedule*Aug 12, 2023

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Pandas `DataFrame.eq(~)`

method returns a DataFrame of booleans where `True`

indicates an entry that is equal to the specified value.

Missing values (`NaN`

) are considered to be distinct, that is, `NaN==NaN`

will evaluate to `False`

.

# Parameters

1. `other`

link | `scalar`

or `sequence`

or `Series`

or `DataFrame`

The value to check for equality.

2. `axis`

link | `string`

or `int`

| `optional`

Whether to perform the comparison along the columns or the rows:

Axis | Description |
---|---|

| Compare each column. |

| Compare each row. |

By default, `axis=1`

.

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 `boolean`

s.

# Examples

Consider the following DataFrame:

```
df = pd.DataFrame({"A":[1,2], "B":[3,4]})df
A B0 1 31 2 4
```

## Element-wise comparisons

To check which values in `df`

equal `3`

:

```
df.eq(3)
A B0 False True1 False False
```

## Row-wise comparisons

To perform row-wise comparisons, pass an array-like structure like follows:

```
df.eq([1,2]) # axis=1
A B0 True False1 False False
```

Here, we are comparing each row of the source DataFrame with `[1,2]`

. This means that we are performing the following pair-wise comparisons:

```
(row one) [1,3] == [1,2] = [True, False](row two) [2,4] == [1,2] = [False, False]
```

## Column-wise comparisons

For your reference, we show the `df`

here again:

```
df
A B0 1 31 2 4
```

To perform column-wise comparisons, pass an array-like structure and set `axis=0`

:

```
df.eq([1,2], axis=0)
A B0 True False1 True False
```

Here, we are comparing each column of the source DataFrame with `[1,2]`

. This means that we are performing the following pair-wise comparisons:

```
(column A) [1,2] == [1,2] = [True, True](column B) [3,4] == [1,2] = [False, False]
```