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