Problem Formulation and Solution Overview

This article will show you how to find the most common element in a Pandas Column.

To make it more interesting, we have the following running scenario:

You have been provided with a downloadable CSV file containing crime statistics for the San Diego area, including their respective NCIC Crime Codes.

Question: How would you determine the most common NCIC Crime Code that occurs in San Diego’s jurisdiction?

We can accomplish this task by one of the following options:

• Method 1: Use Pandas mode()
• Method 2: Use Pandas value_counts()
• Method 3: Use value_counts() and idxmax()
• Method 4: Use value_counts() and keys()
• Method 5: Use Pandas groupby()

Preparation

Before moving forward, please ensure the Pandas library is installed. Click here if you require instructions.

Then, add the following code to the top of each script. This snippet will allow the code in this article to run error-free.

import pandas as pd

After importing the Pandas library, this library is referenced by calling the shortcode (pd).

Method 1: Use Pandas mode()

This example uses the mode() method to determine the single most common crime committed in San Diego on a given day.

df = pd.read_csv('crimes.csv', usecols=['crimedescr'])

max_crime = df['crimedescr'].mode()

print(max_crime)

The above code reads in the crimedescr column from the crimes.csv file downloaded earlier. This saves to the DataFrame df.

Next, the crimedescr column is then accessed, and the mode() method is appended. This method returns a value or set of values that appear most often along a selected axis. The results save to max_crime.

These results are output to the terminal.

So, out of 7,854 rows of crimes committed on a given day for San Diego, the above offense was committed the highest number of times.

The above code only provides us with the name of the most common crime; what if we need the crime name and the respective count?

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code'])

max_crime = df['crimedescr'].mode()

max_count = df['ucr_ncic_code'].mode() print(max_crime)

print(max_count)

The above code is output to the terminal and displays the following.

Now, you are equipped to return to your boss and tell them that 7,000 offenses of 10851 (A) VC TAKE VEH W/O OWNER occurred on a given day in San Diego.

Method 2: Use value_counts()

This example uses the value_counts() function to determine the top 5 most common crimes committed in San Diego on a given day.

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code'])

top5_names = df['crimedescr'].value_counts()[:5].index.tolist()

print(top5_names)

The above code reads in the crimedescr and ucr_ncic_code columns from the crimes.csv file downloaded earlier. This saves to the DataFrame df.

Then, the crimedescr column is accessed, and the value_counts() function is appended. This function returns a series containing the counts of unique values.

However, since slicing is also appended ([:5]), only the top five (5) common crimes are retrieved and then converted to a List. The results save to top5_names.

The above code only provides us with the names of the top 5 most common crimes; what if we need the names and their respective counts?

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code'])

top5 = df['crimedescr'].value_counts()[:5].sort_values(ascending=False)

print(top5)

The above output is sent to the terminal.

A cleaner way to achieve the same results is to use the following code.

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code'])

top5 = df['crimedescr'].value_counts().nlargest(5)

print(top5)

The above code calls the nlargest() method to determine and retrieve the top five (5) common crimes. The output is identical to the above.

A much cleaner and more precise output to send to the boss!

Method 3: Use value_counts() and idxmax()

This example uses value_counts() and idxmax() to determine the single most common crime committed in San Diego on a given day.

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code'])

max_crime = df['crimedescr'].value_counts().idxmax()

print(max_crime)

The above code reads in the crimedescr and ucr_ncic_code columns from the crimes.csv file downloaded earlier. This saves to the DataFrame df.

Then, the crimedescr column is accessed, and the value_counts() function is appended. This function returns a series containing the count of unique values.

Next, idxmax() is appended. This method returns the index of the first occurrence of the maximum index(es) over a selected axis.

The results save to max_crime and are output to the terminal.

Method 4: Use value_counts() and keys()

This example uses value_counts() and keys() to determine the top 5 most common crimes committed in unique grid areas of San Diego on a given day.

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'grid', 'ucr_ncic_code'])

top5_grids = df['grid'].value_counts().keys()[:5]

print(top5_grids)

The above code reads in the crimedescr, grid, and the ucr_ncic_code columns from the crimes.csv file downloaded earlier. This saves to the DataFrame df.

Let’s break the highlighted line down.

If df['grid'].value_counts() was output to the terminal, the following would display (snippet). However, we have added a heading row to make it more understandable, and only five (5) rows are displayed.

Next, the code keys()[:5] is appended. The final output displays as follows.

Method 5: Use groupby()

This examples uses groupby() to group our data on the Crime Code and displays the totals in descending order.

df = pd.read_csv('crimes.csv', usecols=['crimedescr', 'ucr_ncic_code']) res = (df.groupby(['ucr_ncic_code','crimedescr']).size() .sort_values(ascending=False) .reset_index(name='count'))

print(res)

The above code reads in the crimedescr and the ucr_ncic_code columns from the crimes.csv file downloaded earlier. This saves to the DataFrame df.

Next, the groupby() function is called and passed the first argument: df.groupby(['ucr_ncic_code','crimedescr']).size(). If this was output to the terminal at this point, the following would display (snippet).

print(df.groupby(['ucr_ncic_code','crimedescr']).size())

As you can see, the other arguments need to be added to turn this into something usable. Sorting the data in descending order and adding a count column will provide the results we are looking for.

If the original Method 5 code example was output to the terminal, the following would display.

Summary

This article has provided five (5) ways to find the most common element in a Panda Column. These examples should provide you with enough information to select the one that best meets your coding requirements.

Good Luck & Happy Coding!

Programming Humor – Python

Do you encounter the following error message?

TypeError: NoneType is not subscriptable

You’re not alone! This short tutorial will show you why this error occurs, how to fix it, and how to never make the same mistake again.

So, let’s get started!

Summary

Python raises the TypeError: NoneType is not subscriptable if you try to index x[i] or slice x[i:j] a None value. The None type is not indexable, i.e., it doesn’t define the __getitem__() method. You can fix it by removing the indexing or slicing call, or defining the __getitem__ method.

Example

The following minimal example that leads to the error:

x = None

print(x[0])

# TypeError: 'NoneType' object is not subscriptable

You set the variable to the value None. The value None is not a container object, it doesn’t contain other objects. So, the code really doesn’t make any sense—which result do you expect from the indexing operation?

Exercise: Before I show you how to fix it, try to resolve the error yourself in the following interactive shell:

If you struggle with indexing in Python, have a look at the following articles on the Finxter blog—especially the third!

Related Articles:

• Indexing in Python
• Slicing in Python
• Accessing the Index of Iterables in Python

Fixes

You can fix the non-subscriptable TypeError by wrapping the non-indexable values into a container data type such as a list in Python:

x = [None]

print(x[0])

# None

The output now is the value None and the script doesn’t yield an error message anymore.

An alternative is to define the __getitem__() method in your code:

class X: def __getitem__(self, i): return f"Value {i}" variable = X()

print(variable[0])

# Value 0

Related Tutorial: Python __getitem__() magic method

You overwrite the __getitem__ method that takes one (index) argument i (in addition to the obligatory self argument) and returns the i-th value of the “container”.

In our case, we just return a string "Value 0" for the element variable[0] and "Value 10" for the element variable[10].

Full Guide: Python Fixing This Subsctiptable Error (General)

What’s Next?

I hope you’d be able to fix the bug in your code! Before you go, check out our free Python cheat sheets that’ll teach you the basics in Python in minimal time:

This tutorial uses JavaScript’s GeoLocation API to get users’ location. This API call returns location coordinates and other geolocation details.

The following quick example has a function getLatLong() that uses the GeoLocation API. It calls the navigator.geplocation.getCurrentPosition(). This function needs to define the success and error callback function.

On success, it will return the geolocation coordinates array. The error callback includes the error code returned by the API. Both callbacks write the response in the browser console.

User’s location is a privacy sensitive information. We need to be aware of it before working on location access. Since it is sensitive, by default browser and the underlying operating system will not give access to the user’s location information.

Important! The user has to,

1. Explicitly enable location services at operating system level.
2. Give permission for the browser to get location information.

In an earlier article we have seen about how to get geolocation with country by IP address using PHP.

function getLatLong() { // using the JavaScript GeoLocation API // to get the current position of the user // if checks for support of geolocation API if (navigator.geolocation) { navigator.geolocation.getCurrentPosition( function(currentPosition) { console.log(currentPosition)}, function(error) { console.log("Error: " + error.code)} ); } else { locationElement.innerHTML = "JavaScript Geolocation API is not supported by this browser."; }

}

JavaScript GeoLocation API’s getCurrentPosition() function

The below code is an extension of the quick example with Geo Location API. It has a UI that has control to call the JavaScript function to get the current position of the user.

The HTML code has the target to print the location coordinates returned by the API.

The JavaScript fetch callback parameter includes all the geolocation details. The callback function reads the latitude and longitude and shows them on the HTML target via JavaScript.

<!DOCTYPE html>

<html>

<head>

<title>Get User Location from Browser with JavaScript</title>

<link rel='stylesheet' href='style.css' type='text/css' />

<link rel='stylesheet' href='form.css' type='text/css' />

</head>

<body> <div class="phppot-container"> <h1>Get User Location from Browser with JavaScript</h1> <p>This example uses JavaScript's GeoLocation API.</p> <p>Click below button to get your latitude and longitude coordinates.</p> <div class="row"> <button on‌click="getLatLong()">Get Lat Lng Location Coordinates</button> </div> <div class="row"> <p id="location"></p> </div> </div> <script> var locationElement = document.getElementById("location"); function getLatLong() { // using the JavaScript GeoLocation API // to get current position of the user // if checks for support of geolocation API if (navigator.geolocation) { navigator.geolocation.getCurrentPosition( displayLatLong, displayError); } else { locationElement.innerHTML = "JavaScript Geolocation API is not supported by this browser."; } } /** * displays the latitude and longitude from the current position * coordinates returned by the geolocation api. */ function displayLatLong(currentPosition) { locationElement.innerHTML = "Latitude: " + currentPosition.coords.latitude + "<br>Longitude: " + currentPosition.coords.longitude; } /** * displays error based on the error code received from the * JavaScript geolocation API */ function displayError(error) { switch (error.code) { case error.PERMISSION_DENIED: locationElement.innerHTML = "Permission denied by user to get location." break; case error.POSITION_UNAVAILABLE: locationElement.innerHTML = "Location position unavailable." break; case error.TIMEOUT: locationElement.innerHTML = "User location request timed out." break; case error.UNKNOWN_ERROR: locationElement.innerHTML = "Unknown error in getting location." break; } } </script>

</body>

</html>

In the above example script, we have a function for handing errors. It is important to include the function when getting user location via browser. Because, by default, the user’s permission settings will be disabled.

So most of the times, when this script is invoked, we will get errors. So we should have this handler declared and passed as a callback to the getCurrentPosition function. On error, JavaScript will call this error handler.

Geolocation API’s Output

Following is the output format returned by the JavaScript geolocation API. We will be predominantly using latitude and longitude from the result. ‘speed’ may be used when getting dynamic location of the user. We will be seeing about that also at the end of this tutorial.

{ coords = { latitude: 30.123456, longitude: 80.0253546, altitude: null, accuracy: 49, altitudeAccuracy: null, heading: null, speed: null, }, timestamp: 1231234897623
}

View Demo

User location by Geocoding

We can get the user’s location by passing the latitude and longitude like below. There are many different service providers available and below is an example using Google APIs.

const lookup = position => { const { latitude, longitude } = position.coords; fetch(`http://maps.googleapis.com/maps/api/geocode/json?latlng=${latitude},${longitude}`) .then(response => response.json()) .then(data => console.log(data)); //

}

Get dynamic user location from browser using watchPosition()

Here is an interesting part of the tutorial. How will get you a user’s dynamic location, that is when he is on the move.

We should use an another function of GeoLocation API to get dynamic location coordinates on the move. The function watchPosition() is used to do this via JavaScript.

To test this script, run it in a mobile browser while moving in a vehicle to get user’s dynamic location.

I have presented the code part that is of relevance below. You can get the complete script from the project zip, it’s free to download below.

var locationElement = document.getElementById("location"); function getLatLong() { // note the usage of watchPosition, this is the difference // this returns the dynamic user position from browser if (navigator.geolocation) { navigator.geolocation.watchPosition(displayLatLong, displayError); } else { locationElement.innerHTML = "JavaScript Geolocation API is not supported by this browser."; } }

View Demo Download

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