#for multiple columns
min_vals = df[["A","B","C"]].min() #can add how much ever columns
max_vals = df[["D","E","F"]].max() #can add how much ever columns
#for single column
min_val = df["Column"].min()
max_val = df["Column"].max()
#to refer to all columns
min_val = df[:].min()
max_val = df[:].max()
#use this to get the index of the max value of a column
max_index = column.idxmax()
max_value_column = df["column_name"].max()
df = {'a': 3, 'b':4, 'c':5}
df.max() #max() gives you the maximum value in the series.
#Output
5
df.idxmax() #idx() gives you the index of the maximum values.
#Output
c
#NB: The same applies to min() and idxmin().
import pandas as pd
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 50)
pd.set_option('display.width', 1500)
# displays max number of columns and rows
import pandas as pd
pd.options.display.max_rows = 999
pd.options.display.max_columns = 999
max_value = df.to_numpy().max()
max(df.max(axis=1))