# Pandas Get Floor or Ceil of Series

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• Post category:Pandas

We can get the floor or ceil (Ceiling) values from the pandas Series by using `series.clip()`, NumPy’s `floor()` and `ceil() `functions. In simple words, the floor value is always less than or equal to the given value, and the ceiling value is always greater than or equal to the given value. In this article, I will explain how we can get the floor or ceiling of a pandas series in python with several examples.

## 1. Quick Examples of Floor or Ceil of a Series

If you are in a hurry, below are some quick examples of how to floor or ceiling of series in python.

``````
# Below are some quick examples

# Example 1: get the floor values of pandas series
ser2 = np.floor(ser)

# Example 2: get the ceil values of pandas series
ser2 = np.ceil(ser)

# Example 3: use pandas.series.clip() function to get lower values
ser2 = ser.clip(lower=0)

# Example 4: use pandas.series.clip() function to get upper values
ser2 = ser.clip(upper=0)
``````

## 2. Initialize Pandas Series

Pandas Series is a one-dimensional, Index-labeled data structure available only in the Pandas library. It can store all the datatypes such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

Now, let’s create pandas series using a list of values.

``````
import pandas as pd
import numpy as np

# Create the Series
ser = pd.Series([2.3,3.5,-1.3,5.7,4.8,-6.8])
print(ser)
``````

Yields below output.

``````
# Output:
0    2.3
1    3.5
2   -1.3
3    5.7
4    4.8
5   -6.8
dtype: float64
``````

## 3. Get the Floor Values of the Pandas Series

Using numpy.floor() function we can get the floor of each value in the Series. `floor()` function takes Series as a parameter and returns the floor value of each Series element with a float data type. The floor value of the scalar `x` is the largest integer `y`, such that `y<=x`.

``````
# Get the floor values of pandas series
value = np.floor(ser)
print(value)
``````

Yields below output.

``````
# Output:
0    2.0
1    3.0
2   -2.0
3    5.0
4    4.0
5   -7.0
dtype: float64
``````

## 4. Get the Ceil Values of Pandas Series

Using numpy.ceil() function we can get the ceiling of each value in the Series. The ceil of the scalar `x` is the smallest integer `i`, such that `i >= x`. In simple words, the ceil value is always greater than equal to the given value.

``````
# Get the ceil values of pandas series
ser2 = np.ceil(ser)
print(ser2)
``````

Yields below output.

``````
# Output:
0    3.0
1    4.0
2   -1.0
3    6.0
4    5.0
5   -6.0
dtype: float64
``````

## 5. Use pandas.Series.clip() Function to Get Lower & Upper Values

Pandas `Series.clip()` is used to get the lower & upper values of the series. If you pass `lower=0` into `clip()` function, it will override the least values with zero’s. If you pass `upper=0` into `clip()` function, It will override the heighest values with zeros.

``````
# Use pandas.series.clip() function to get lower values
ser2 = ser.clip(lower=0)
print(ser2)

# Output:
# 0    2.3
# 1    3.5
# 2    0.0
# 3    5.7
# 4    4.8
# 5    0.0
# dtype: float64

# Use pandas.series.clip() function to get upper values
ser2 = ser.clip(upper=0)
print(ser2)

# Output:
# 0    0.0
# 1    0.0
# 2   -1.3
# 3    0.0
# 4    0.0
# 5   -6.8
# dtype: float64
``````

## 6. Complete Example For Floor or Ceiling of a Series

``````
import pandas as pd
import numpy as np

# Create the Series
ser = pd.Series([2.3,3.5,-1.3,5.7,4.8,-6.8])
print(ser)

# Get the floor values of pandas series
ser2 = np.floor(ser)
print(ser2)

# Get the ceil values of pandas series
ser2 = np.ceil(ser)
print(ser2)

# Use pandas.series.clip() function to get lower values
ser2 = ser.clip(lower=0)
print(ser2)

# Use pandas.series.clip() function to get upper values
ser2 = ser.clip(upper=0)
print(ser2)
``````

## 7. Conclusion

In this article, I have explained how to floor or ceil (ceiling) of a pandas series in python using `series.clip()`, `numpy.floor()`, and `numpy.ceil()` functions with examples.

Happy Learning !!

## References

### Malli

I am Mallikarjuna an experienced technical writer with a passion for translating complex Python concepts into clear, concise, and user-friendly documentation. Over the years, I have written hundreds of articles in Pandas, NumPy, Python, and I take pride in my ability to bridge the gap between technical experts and end-users by delivering well-structured, accessible, and informative content. 