In Pandas, Panel is a very important container for three-dimensional data. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data.
Panel.clip_lower() function is used to return copy of the input with values below a threshold truncated.
Syntax: Panel.clip_lower(threshold, axis=None, inplace=False)
Parameters:
threshold : Minimum value allowed. All values below threshold will be set to this value.
float : every value is compared to threshold.
array-like : The shape of threshold should match the object it’s compared to.
axis : Align self with threshold along the given axis.
inplace : Whether to perform the operation in place on the data.Returns: same type as input.
Code #1: Creating a Panel using from_dict()
# importing pandas module import pandas as pd import numpy as np df1 = pd.DataFrame({'a': ['Geeks', 'For', 'neveropen'], 'b': np.random.randn(3)}) data = {'item1':df1, 'item2':df1} # creating Panel panel = pd.Panel.from_dict(data, orient ='minor') print(panel, "\n") |
Output:
Code #2: Using clip_lower()
# importing pandas module import pandas as pd import numpy as np df1 = pd.DataFrame({'a': ['Geeks', 'For', 'neveropen'], 'b': np.random.randn(3)}) data = {'item1':df1, 'item2':df1} # creating Panel panel = pd.Panel.from_dict(data, orient ='minor') print(panel, "\n") print(panel['b'], '\n') df2 = pd.DataFrame({'b': [11, 12, 13]}) print(panel['b'].clip_lower(df2['b'], axis = 0)) |
Output:
Code #3:
# creating an empty panel import pandas as pd import numpy as np data = {'Item1' : pd.DataFrame(np.random.randn(7, 4)), 'Item2' : pd.DataFrame(np.random.randn(4, 5))} pen = pd.Panel(data) print(pen['Item1'], '\n') p = pen['Item1'][0].clip_lower(np.random.randn(7)) print(p) |
Output:
