# Pandas Series - count() function

The Pandas Series count() function is used to count non-NA/null observations in the Series. The values None, NaN, NaT, and optionally pandas.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

### Syntax

```Series.count(level=None)
```

### Parameters

 `level` `Optional. `Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series. A str specifies the level name.

### Return Value

Returns int or Series (if level specified), indicating number of non-null values in the Series.

### Example: using count() on a Series

In the example below, the count() function is used to get the count of non-NA values in the series.

```import pandas as pd
import numpy as np

idx = pd.MultiIndex.from_arrays([
['even', 'even', 'even',
'odd', 'odd', 'odd']],
names=['DataType'])

x = pd.Series([10, 20, np.NaN, 5, np.NaN, np.NaN],
name='Numbers', index=idx)

print("The Series contains:")
print(x)
print("\nCount of non-NA values", x.count())
print("\nCount of non-NA values with level='DataType':\n",
x.count(level='DataType'))
print("\nCount of non-NA values with level=0:\n",
x.count(level=0))
```

The output of the above code will be:

```The Series contains:
DataType
even        10.0
even        20.0
even         NaN
odd          5.0
odd          NaN
odd          NaN
Name: Numbers, dtype: float64

Count of non-NA values 3

Count of non-NA values with level='DataType':
DataType
even    2
odd     1
Name: Numbers, dtype: int64

Count of non-NA values with level=0:
DataType
even    2
odd     1
Name: Numbers, dtype: int64
```

### Example: using count() on selected series in a DataFrame

Similarly, the count() function can be applied on selected series/column of a given DataFrame. Consider the following example.

```import pandas as pd
import numpy as np

info = pd.DataFrame({
"Person": ["John", "Mary", "Jo", "Sam"],
"Age": [25, 24, 30, 28],
"Bonus": ["10K", np.nan, "10K", "9K"]
})

print(info)

#using count on 'Person' series
print("\ncount on Person returns:")
print(info['Person'].count())
```

The output of the above code will be:

```  Person  Age Bonus
0   John   25   10K
1   Mary   24   NaN
2     Jo   30   10K
3    Sam   28    9K

count on Person returns:
4
```

❮ Pandas Series - Functions

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