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You can simply go for: df.describe () that will provide you with all the relevant details you need, but to find the min, max or average value of a particular column (say 'weights' in your case), use: df['weights'].mean(): For average value. df['weights'].max(): For maximum value.
3 mar 2014 · Take the pandas.DataFrame.mean for example: axis{index (0), columns (1)} Axis for the function to be applied on. For Series this parameter is unused and defaults to 0. For DataFrames, specifying axis=None will apply the aggregation across both axes.
DataFrame.mean(axis=0, skipna=True, numeric_only=False, **kwargs)[source] #. Return the mean of the values over the requested axis. Parameters: axis{index (0), columns (1)} Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
Series.mean(axis=0, skipna=True, numeric_only=False, **kwargs)[source] #. Return the mean of the values over the requested axis. Parameters: axis{index (0)} Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
17 maj 2024 · Pandas dataframe.mean () function returns the mean of the values for the requested axis. If the method is applied on a pandas series object, then the method returns a scalar value which is the mean value of all the observations in the Pandas Dataframe.
By specifying the column axis (axis='columns'), the mean() method searches column-wise and returns the mean value for each row. Syntax dataframe .mean(axis, skipna, level, numeric_only, kwargs )
20 lut 2024 · Use the following syntax: DataFrame.mean(axis=None, skipna=True, level=None, numeric_only=True, **kwargs) Parameters: axis: {0 or ‘index’, 1 or ‘column’} – Specify the axis for the mean calculation. skipna: Boolean, default True – Whether to exclude NA/null values.
