Python | Pandas dataframe.clip_upper()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.clip_upper() is used to trim values at specified input threshold. We use this function to trim all the values above the threshold of the input value to the specified input value.

Syntax: DataFrame.clip_upper(threshold, axis=None, inplace=False) Parameters: threshold : float or array_like float : every value is compared to threshold. array-like : The shape of threshold should match the object it’s compared to. When self is a Series, threshold should be the length. When self is a DataFrame, threshold should 2-D and the same shape as self for axis=None, or 1-D and the same length as the axis being compared. axis : Align object with threshold along the given axis. inplace : Whether to perform the operation in place on the data. Returns: clipped : same type as input

Example #1: Use clip_upper() function to trim values of a data frame above a given threshold value. Python3 1==

# importing pandas as pd
import pandas as pd

# Creating a dataframe using dictionary
df = pd.DataFrame({"A":[-5, 8, 12, -9, 5, 3],
                   "B":[-1, -4, 6, 4, 11, 3],
                   "C":[11, 4, -8, 7, 3, -2]})

# Printing the data frame for visualization
df

Now trim all the values above 8 to 8. Python3 1==

# Clip all values below 2
df.clip_upper(8)

Output :   Example #2: Use clip_upper() function to clips values in a dataframe with specific value for each cell of the dataframe. For this purpose, we can use a numpy array, but the shape of array must be same as that of the dataframe. Python3 1==

# importing pandas as pd
import pandas as pd

# Creating a dataframe using dictionary

df = pd.DataFrame({"A":[-5, 8, 12, -9, 5, 3], 
                   "B":[-1, -4, 6, 4, 11, 3],
                   "C":[11, 4, -8, 7, 3, -2]})

# upper limit for each individual column element.
limit = np.array([[10, 2, 8], [3, 5, 3], [2, 4, 6],
                  [11, 2, 3], [5, 2, 3], [4, 5, 3]])

# Print upper_limit
limit

Now apply these limits on the dataframe. Python3 1==

# applying different limit value
# for each cell in the dataframe
df.clip_upper(limit)

Output : Each cell value has been trimmed based on the corresponding upper limit applied.