# Pandas Series: equals() function

## Test Pandas objects contain the same elements

The equals() function is used to test whether two Pandas objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do not need to have the same type, but the elements within the columns must be the same dtype.

**Syntax:**

Series.equals(self, other)

**Parameters:**

Name | Description | Type/Default Value | Required / Optional |
---|---|---|---|

other | The other Series or DataFrame to be compared with the first. | Series or DataFrame | Required |

**Returns: **bool

True if all elements are the same in both objects, False otherwise.

**Example :**

Python-Pandas Code:

```
import numpy as np
import pandas as pd
df = pd.DataFrame({2: [20], 3: [30]})
df
```

Output:

2 3 0 20 30

**Example - DataFrames df and exactly_equal have the same types and values for their elements and column labels,
which will return True:**

Python-Pandas Code:

```
import numpy as np
import pandas as pd
exactly_equal = pd.DataFrame({2: [20], 3: [30]})
exactly_equal
```

Output:

2 3 0 20 30

Python-Pandas Code:

```
import numpy as np
import pandas as pd
df = pd.DataFrame({2: [20], 3: [30]})
exactly_equal = pd.DataFrame({2: [20], 3: [30]})
df.equals(exactly_equal)
```

Output:

True

**Example - DataFrames df and different_column_type have the same element types and values, but have different
types for the column labels, which will still return True:**

Python-Pandas Code:

```
import numpy as np
import pandas as pd
different_column_type = pd.DataFrame({2.0: [20], 3.0: [30]})
different_column_type
```

Output:

2.0 3.0 0 20 30

Python-Pandas Code:

```
import numpy as np
import pandas as pd
df = pd.DataFrame({2: [20], 3: [30]})
different_column_type = pd.DataFrame({2.0: [20], 3.0: [30]})
df.equals(different_column_type)
```

Output:

True

**Example - DataFrames df and different_data_type have different types for the same values for their elements,
and will return False even though their column labels are the same values and types:**

Python-Pandas Code:

```
import numpy as np
import pandas as pd
df = pd.DataFrame({2: [20], 3: [30]})
different_data_type = pd.DataFrame({2: [20.0], 3: [30.0]})
df.equals(different_data_type)
```

Output:

False

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