Hướng dẫn get max value python
Return the maximum of the values over the requested axis. Nội dung chính If you want the index of the maximum, use Axis for the function to be applied on. skipnabool, default TrueExclude NA/null values when computing the result. levelint or level name, default NoneIf the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. numeric_onlybool, default NoneInclude only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. **kwargsAdditional keyword arguments to be passed to the function. ReturnsSeries or DataFrame (if level specified)Examples >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 ❮ DataFrame Reference ExampleReturn the highest value for each column: import pandas as pd data = [[10, 18, 11], [13, 15, 8], [9, 20, 3]] df = pd.DataFrame(data) print(df.max()) Try it Yourself » Definition and UsageThe By specifying the
column axis ( Syntaxdataframe.max(axis, skipna, level, numeric_only, kwargs) ParametersThe
Return ValueA Series with the maximum values. If the level argument is specified, this method will return a DataFrame object. This function does NOT make changes to the original DataFrame object. ❮ DataFrame Reference Pandas dataframes are great for analyzing and manipulating data. In this tutorial, we will look at how to get the max value in one or more columns of a pandas dataframe with the help of some examples. Pandas max() functionYou can use the pandas # df is a pandas dataframe # max value in a column df['Col'].max() # max value for multiple columns df[['Col1', 'Col2']].max() # max value for each numerical column in the dataframe df.max(numeric_only=True) # max value in the entire dataframe df.max(numeric_only=True).max() It returns the maximum value or values depending on the input and the axis (see the examples below). ExamplesLet’s look at some use-case of the pandas import numpy as np import pandas as pd # create a pandas dataframe df = pd.DataFrame({ 'Name': ['Neeraj Chopra', 'Jakub Vadlejch', 'Vitezslav Vesely', 'Julian Weber', 'Arshad Nadeem'], 'Country': ['India', 'Czech Republic', 'Czech Republic', 'Germany', 'Pakistan'], 'Attempt1': [87.03, 83.98, 79.79, 85.30, 82.40], 'Attempt2': [87.58, np.nan, 80.30, 77.90, np.nan], 'Attempt3': [76.79, np.nan, 85.44, 78.00, 84.62], 'Attempt4': [np.nan, 82.86, np.nan, 83.10, 82.91], 'Attempt5': [np.nan, 86.67, 84.98, 85.15, 81.98], 'Attempt6': [84.24, np.nan, np.nan, 75.72, np.nan] }) # display the dataframe df Output: Here we created a dataframe containing the scores of the top five performers in the men’s javelin throw event final at the Tokyo 2020 Olympics. The attempts represent the throw of the javelin in meters. 1. Max value in a single pandas columnTo get the maximum value in a pandas column, use the max() function as follows. For example, let’s get the maximum value achieved in the first attempt. # max value in Attempt1 print(df['Attempt1'].max()) Output: 87.03 We get 87.03 meters as the maximum distance thrown in the “Attemp1” Note that you can get the index corresponding to the max value with the pandas idxmax() function. Let’s get the name of the athlete who threw the longest in the first attempt with this index. # index corresponding max value i = df['Attempt1'].idxmax() print(i) # display the name corresponding this index print(df['Name'][i]) Output: 0 Neeraj Chopra You can see that the max value corresponds to “Neeraj Chopra”. 2. Max value in two pandas columnsYou can also get the max value of multiple pandas columns with the pandas min() function. For example, let’s find the maximum values in “Attempt1” and “Attempt2” respectively. # get max values in columns "Attempt1" and "Attempt2" print(df[['Attempt1', 'Attempt2']].max()) Output: Attempt1 87.03 Attempt2 87.58 dtype: float64 Here, created a subset dataframe with the columns we wanted and then applied the max() function. We get the maximum value for each of the two columns. 3. Max value for each column in the dataframeSimilarly, you can get the max value for each column in the dataframe. Apply the max function over the entire dataframe instead of a single column or a selection of columns. For example, # get max values in each column of the dataframe print(df.max()) Output: Name Vitezslav Vesely Country Pakistan Attempt1 87.03 Attempt2 87.58 Attempt3 85.44 Attempt4 83.1 Attempt5 86.67 Attempt6 84.24 dtype: object We get the maximum values in each column of the dataframe df. Note that we also get max values for text columns based on their string comparisons in python. If you only want the max values for all the numerical columns in the dataframe, pass # get max values of only numerical columns print(df.max(numeric_only=True)) Output: Attempt1 87.03 Attempt2 87.58 Attempt3 85.44 Attempt4 83.10 Attempt5 86.67 Attempt6 84.24 dtype: float64 4. Max value between two pandas columnsWhat if you want to get the maximum value between two columns? # max value over two columns print(df[['Attempt1', 'Attempt2']].max().max()) Output: 87.58 We get 87.58 as the maximum distance considering the first and the second attempts together. 5. Max value in the entire dataframeYou can also get the single biggest value in the entire dataframe. For example, let’s get the biggest value in the dataframe df irrespective of the column. # mav value over the entire dataframe print(df.max(numeric_only=True).max()) Output: 87.58 Here we apply the pandas max() function twice. First time to get the max values for each numeric column and then to get the max value among them. For more on the pandas max() function, refer to its documentation. With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5
How do you find the maximum and minimum of a column in Python?“find max and min values in python column” Code Answer's. 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.. min_val = df["Column"]. min(). max_val = df["Column"]. max(). min_val = df[:]. min(). max_val = df[:]. max(). How do you find the maximum index of a DataFrame?idxmax() function returns index of first occurrence of maximum over requested axis. While finding the index of the maximum value across any index, all NA/null values are excluded. Example #1: Use idxmax() function to function to find the index of the maximum value along the index axis. How do you find the top 5 values in Python?Python's Pandas module provide easy ways to do aggregation and calculate metrics. Finding Top 5 maximum value for each group can also be achieved while doing the group by. The function that is helpful for finding the Top 5 maximum value is nlargest(). How do you get top 10 values in pandas?How to Get Top 10 Highest or Lowest Values in Pandas. Step 1: Create Sample DataFrame. ... . Step 2: Get Top 10 biggest/lowest values for single column. ... . Step 3: Get Top 10 biggest/lowest values - duplicates. ... . Step 4: Get Top N values in multiple columns. ... . Step 5: How do nsmallest and nlargest work.. |