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pandas.DataFrame.loc # property DataFrame.loc [source] # Access a group of rows and columns by label (s) or a boolean array. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are:
1 gru 2023 · Pandas DataFrame.loc attribute accesses a group of rows and columns by label (s) or a boolean array in the given Pandas DataFrame. Syntax: DataFrame.loc. Parameter : None. Returns : Scalar, Series, DataFrame.
Definition and Usage. The loc property gets, or sets, the value (s) of the specified labels. Specify both row and column with a label. To access more than one row, use double brackets and specify the labels, separated by commas: df.loc[["Sally", "John"]] Specify columns by including their labels in another list: df.loc[["Sally", "John"], ["age", .
df.loc[['B', 'A'], ['X']] X B 3 A 1 boolean arrays whose elements are True or False and whose length matches the length of the respective index. In this case, loc simply grabs the rows (or columns) in which the boolean array is True .
The loc[] property in Pandas is used to select data from a DataFrame based on labels or conditions. Example import pandas as pd data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Los Angeles', 'Chicago']} df = pd.DataFrame(data)
24 lut 2024 · Learn how to use pandas.DataFrame.loc[] to select and modify data based on label information. See six examples of basic and advanced use cases, such as slicing, conditional selection, and combining with other methods.
property DataFrame.loc ¶. Access a group of rows and columns by label (s) or a boolean array. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are: A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
