assume series Show
Get quantiles for
OR In this tutorial, you’ll learn how to use the Pandas By the end of this tutorial, you’ll have learned:
The Quick Answer: Use Pandas
What is a Percentile?A percentile refers to a number where certain percentages fall below that number. For example, if we calculate the 90th percentile, then we return a number where 90% of all other numbers fall below that number. This has many useful applications, such as in education. Scoring the in 90th percentile does not mean you scored 90% on a test, but that you scored better than 90% of other test takers. A quartile, however, splits the data into four equal chunks of data, split into 25% values. The quartile, therefore, is really splitting the data into percentiles of 0%, 25%, 50%, and 75%. Being able to calculate a percentile has many useful applications, such as working with outliers. Because outliers have a large effect on machine learning models that may skew their performance, you may want to be aware of them. For example, you want want to know how many values fall in and outside of the 5th and 95th percentile to see how much skew of your data to expect. Let’s get started with learning how to calculate a percentile in Pandas using the Loading a Sample Pandas DataframeLet’s start off by loading a sample Pandas Dataframe. If you have your own data, feel free to use that. However, if you want to follow along with this tutorial line by line, copy the code below to generate our dataframe: # Loading a Sample Pandas Dataframe import pandas as pd df = pd.DataFrame.from_dict({ 'Student': ['Nik', 'Kate', 'Kevin', 'Evan', 'Jane', 'Kyra', 'Melissa'], 'English': [90, 95, 75, 93, 60, 85, 75], 'Chemistry': [95, 95, 75, 65, 50, 85, 100], 'Math': [100, 95, 50, 75, 90, 50, 80] }) print(df.head()) # Returns: # Student English Chemistry Math # 0 Nik 90 95 100 # 1 Kate 95 95 95 # 2 Kevin 75 75 50 # 3 Evan 93 65 75 # 4 Jane 60 50 90 We can see that we’ve loaded a Pandas Dataframe covering student’s grades. We have a single Now, let’s dive into
understanding how the Pandas Pandas Quantile Method OverviewThe Pandas Let’s take a look at what the method looks like and what parameters the # Understanding the Pandas .quantile() method to calculate percentiles df.quantile( q=0.5, # The percentile to calculate axis=0, # The axis to calculate the percentile on numeric_only=True, # To calculate only for numeric columns interpolation='linear' # The type of interpolation to use when the quantile is between 2 values ) Let’s take a look at the different
parameters that the Pandas
Now that you’ve learned about the different arguments available, let’s jump in and calculate a percentile for a given column. Use Pandas Quantile to Calculate a Single PercentileIn this section, you’ll learn how to calculate a single percentile on a Pandas Dataframe column using the # Generate a single percentile with df.quantile() print(df['English'].quantile()) # Returns: 85.0 By default, Pandas will use a parameter of # Generate a single percentile with df.quantile() print(df['English'].quantile(q=0.9)) # Returns: 93.8 We can see that by passing in only a single value into the Use Pandas Quantile to Calculate Multiple PercentilesThere may be many times that you want to calculate a number of different percentiles for a Pandas column. The If we wanted to calculate multiple percentiles, we simply pass in a list of values for the different percentiles we want to calculate. Let’s see what this looks like: # Generate multiple percentiles with df.quantile() print(df['English'].quantile(q=[0.1, 0.9])) # Returns: # 0.1 69.0 # 0.9 93.8 # Name: English, dtype: float64 This returns a Pandas series containing the different percentile values. If we wanted to access a single value in this series, we can simply access it by selecting its index. Let’s see how we can select the 90th percentile in our series: # Generate multiple percentiles with df.quantile() and selecting one print(df['English'].quantile(q=[0.1, 0.9])[0.9]) # Returns: 93.8 This is a helpful method if you want to be able to calculate multiple percentiles in one go but use the values of these percentiles programatically. In the next section, you’ll learn how to use Pandas to calculate percentiles of an entire dataframe. In many cases, you may want to calculate percentiles of all columns in a dataframe. In our example, we have columns that display grades for different students in a variety of subjects. Instead of needing to calculate the percentiles for each subject, we can simply calculate the percentiles for the entire dataframe, thereby speeding up our workflow. Let’s see how this works by calculating the 90th percentile for every column: # Calculate Percentile for a Pandas Dataframe print(df.quantile(q=0.9)) # Returns: # English 93.8 # Chemistry 97.0 # Math 97.0 # Name: 0.9, dtype: float64 We can see how easy it was to calculate a single percentile for all columns in a Pandas Dataframe. By default, Pandas will calculate the percentiles only for numeric columns, since there’s no way to calculate it for strings or other data types. If you wanted to calculate the values for dates and timedeltas, you can toggle the If you wanted to calculate multiple
percentiles for an entire dataframe, you can pass in a list of values to calculate. Let’s calculate a number of different percentiles using Pandas’ English Chemistry Math 0.1 69.0 59.0 50.0 0.5 85.0 85.0 80.0 0.9 93.8 97.0 97.0 We can see that Pandas actually returns a dataframe containing the breakout of percentiles by the different columns. We can use In the next section, you’ll learn how to modify how Pandas interpolates percentiles when the percentile falls between two values. Use Pandas Quantile to Calculate Percentiles and Modify InterpolationWhen calculating a percentile, you may encounter a situation where the percentile falls between two values. In these cases, a decision needs to be made as to how to calculate the percentile. For example, you could select the midpoint between the two values, the lower / upper bound, or an interpolated value. This is where the Pandas also provides a number of options to modify this behaviour. These options are broken out in the table below, assuming two values i and j:
Let’s see how these values might differ for a single column: # Interpolating Percentiles in Different Ways linear = df['Math'].quantile(q=0.9, interpolation='linear') lower = df['Math'].quantile(q=0.9, interpolation='lower') higher = df['Math'].quantile(q=0.9, interpolation='higher') nearest = df['Math'].quantile(q=0.9, interpolation='nearest') midpoint = df['Math'].quantile(q=0.9, interpolation='midpoint') print('linear returns: ', linear) print('lower returns: ', lower) print('higher returns: ', higher) print('nearest returns: ', nearest) print('midpoint returns: ', midpoint) # Returns: # linear returns: 97.0 # lower returns: 95 # higher returns: 100 # nearest returns: 95 # midpoint returns: 97.5 Being able to choose the type of interpolation, we can customize the results in a way that meets our needs. ConclusionIn this tutorial, you learned how to use the Pandas To learn more about the Pandas Additional DocumentationSome other relevant articles are provided below:
How do you find the 75th percentile of a column in Python?“how to find the 75th percentile in pandas” Code Answer. import pandas as pd.. import random.. A = [ random. randint(0,100) for i in range(10) ]. B = [ random. randint(0,100) for i in range(10) ]. df = pd. DataFrame({ 'field_A': A, 'field_B': B }). How do you find the percentile of a DataFrame in Python?Pandas Quantile Method Overview. q=[0.5] : a float or an array that provides the value(s) of quantiles to calculate.. axis=[0] : the axis to calculate the percentiles on (0 for row-wise and 1 for column-wise). numeric_only=[True] : is set to False , calculate the values for datetime and timedelta columns as well.. How do I find the percentile of a column in pandas?To find percentiles of a numeric column in a DataFrame, or the percentiles of a Series in pandas, the easiest way is to use the pandas quantile() function. You can also use the numpy percentile() function.
How do you calculate quantile of a column in Python?Pandas DataFrame quantile() Method
The quantile() method calculates the quantile of the values in a given axis. Default axis is row. By specifying the column axis ( axis='columns' ), the quantile() method calculates the quantile column-wise and returns the mean value for each row.
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