# Pandas Series - ne() function

The Pandas ne() function compares series and other, element-wise for not equal to and returns the comparison result. It is equivalent to series != other, but with support to substitute a fill_value for missing data as one of the parameters.

### Syntax

```Series.ne(other, level=None, fill_value=None)
```

### Parameters

 `other` `Required. `Specify scalar value or Series. `level` `Optional. `Specify int or name to broadcast across a level, matching Index values on the passed MultiIndex level. Default is None. `fill_value` `Optional. `Specify value to fill existing missing (NaN) values, and any new element needed for successful Series alignment. If data in both corresponding Series locations is missing the result will be missing. Default is None.

### Return Value

Returns the result of the comparison.

### Example: Comparing Series with a scalar value

In the example below, the ne() function is used to compare a Series with a given scalar value.

```import pandas as pd
import numpy as np

x = pd.Series([10, 20, 30, 40, 50])

print("The Series contains:")
print(x)

#comparing Series != 30
print("\nx.ne(30) returns:")
print(x.ne(30))
```

The output of the above code will be:

```The Series contains:
0    10
1    20
2    30
3    40
4    50
dtype: int64

x.ne(30) returns:
0     True
1     True
2    False
3     True
4     True
dtype: bool
```

### Example: Comparing Series with a Series

A series can be compared with another series element-wise for Not equal to of. Consider the following example:

```import pandas as pd
import numpy as np

x = pd.Series([10, np.NaN, 30, 40, 50],
index=['A', 'B', 'C', 'D', 'E'])
y = pd.Series([5, 20, 30, np.NaN, 65],
index=['A', 'B', 'C', 'D', 'E'])

print("The x contains:")
print(x)

print("\nThe y contains:")
print(y)

#calculating x != y
print("\nx.ne(y, fill_value=0) returns:")
print(x.ne(y, fill_value=0))
```

The output of the above code will be:

```The x contains:
A    10.0
B     NaN
C    30.0
D    40.0
E    50.0
dtype: float64

The y contains:
A     5.0
B    20.0
C    30.0
D     NaN
E    65.0
dtype: float64

x.ne(y, fill_value=0) returns:
A     True
B     True
C    False
D     True
E     True
dtype: bool
```

### Example: using ne() on columns of a DataDrame

The ne() function can be applied in a DataFrame to get the result of comparing for Not equal to of two series/column element-wise. Consider the following example.

```import pandas as pd
import numpy as np

df = pd.DataFrame({
"col1": [10, 20, 30, 40, 50],
"col2": [10, 15, 30, 45, 55]
})

print("The DataFrame is:")
print(df)

#calculating 'col1' != 'col2'
df['Result'] = df['col1'].ne(df['col2'])

print("\nThe DataFrame is:")
print(df)
```

The output of the above code will be:

```The DataFrame is:
col1  col2
0    10    10
1    20    15
2    30    30
3    40    45
4    50    55

The DataFrame is:
col1  col2  Result
0    10    10   False
1    20    15    True
2    30    30   False
3    40    45    True
4    50    55    True
```

❮ Pandas Series - Functions

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