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Python | Pandas dataframe.clip_lower()

  • Last Updated : 20 Nov, 2018
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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_lower() is used to trim values at specified input threshold. We use this function to trim all the values below the threshold of the input value.

Syntax: DataFrame.clip_lower(threshold, axis=None, inplace=False)

Parameters:
threshold : numeric 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 self 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_lower() function to trim values of a data frame below 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 below 2 to 2.




# Clip all values below 2
df.clip_lower(2)

Output :

 

Example #2: Use clip_lower() 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]})
  
# lower limit for each individual column element.
limit = np.array([[1, 2, 3], [10, 12, 3], [1, 4, 3],
                  [1, 2, 3], [1, 2, 3], [1, 2, 3]])
  
# Print lower_limit
limit

Now apply these limits on the dataframe




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

Output :

Each cell value has been trimmed based on the corresponding lower limit applied.

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