When we’re doing data analysis with Python, we might sometimes want to add a column to a pandas DataFrame based on the values in other columns of the DataFrame.
Although this sounds straightforward, it can get a bit complicated if we try to do it using an if-else conditional. Thankfully, there’s a simple, great way to do this using numpy!
To learn how to use it, let’s look at a specific data analysis question. We’ve got a dataset of more than 4,000 Dataquest tweets. Do tweets with attached images get more likes and retweets? Let’s do some analysis to find out!
We’ll start by importing pandas and numpy, and loading up our dataset to see what it looks like. (If you’re not already familiar with using pandas and numpy for data analysis, check out).
import pandas as pd import numpy as np df = pd.read_csv('dataquest_tweets_csv.csv') df.head()
We can see that our dataset contains a bit of information about each tweet, including:
date— the date the tweet was posted
time— the time of day the tweet was posted
tweet— the actual text of the tweet
mentions— any other twitter users mentioned in the tweet
photos— the url of any images included in the tweet
replies_count— the number of replies on the tweet
retweets_count— the number of retweets of the tweet
likes_count— the number of likes on the tweet
We can also see that the
photos data is formatted a bit oddly.
Adding a Pandas Column with a True/False Condition Using np.where()
For our analysis, we just want to see whether tweets with images get more interactions, so we don’t actually need the image URLs. Let’s try to create a new column called
hasimage that will contain Boolean values —
True if the tweet included an image and
False if it did not.
To accomplish this, we’ll use. This function takes three arguments in sequence: the condition we’re testing for, the value to assign to our new column if that condition is true, and the value to assign if it is false. It looks like this:
np.where(condition, value if condition is true, value if condition is false)
In our data, we can see that tweets without images always have the value
 in the
photos column. We can use information and
np.where() to create our new column,
hasimage, like so:
df['hasimage'] = np.where(df['photos']!= '', True, False) df.head()
Above, we can see that our new column has been appended to our data set, and it has correctly marked tweets that included images as
True and others as
Now that we’ve got our
hasimage column, let’s quickly make a couple of new DataFrames, one for all the image tweets and one for all of the no-image tweets. We’ll do that using a Boolean filter:
image_tweets = df[df['hasimage'] == True] no_image_tweets = df[df['hasimage'] == False]
Now that we’ve created those, we can use built-in pandas math functions like
.mean() to quickly compare the tweets in each DataFrame.
print() statements to make the results a little easier to read. We’ll also need to remember to use
str() to convert the result of our
.mean() calculation into a string so that we can use it in our print statement:
## LIKES print('Average likes, all tweets: ' + str(df['likes_count'].mean())) print('Average likes, image tweets: ' + str(image_tweets['likes_count'].mean())) print('Average likes, no image tweets: ' + str(no_image_tweets['likes_count'].mean())) print('\n') ## RETWEETS print('Average RTs, all tweets: ' + str(df['retweets_count'].mean())) print('Average RTs, image tweets: ' + str(image_tweets['retweets_count'].mean())) print('Average RTs, no image tweets: ' + str(no_image_tweets['retweets_count'].mean()))
Average likes, all tweets: 6.209759328770148 Average likes, image tweets: 14.21042471042471 Average likes, no image tweets: 5.176514584891549 Average RTs, all tweets: 1.5553102230072864 Average RTs, image tweets: 3.5386100386100385 Average RTs, no image tweets: 1.2991772625280478
Based on these results, it seems like including images may promote more Twitter interaction for Dataquest. Tweets with images averaged nearly three times as many likes and retweets as tweets that had no images.
Adding a Pandas Column with More Complicated Conditions
That approach worked well, but what if we wanted to add a new column with more complex conditions — one that goes beyond True and False?
For example, to dig deeper into this question, we might want to create a few interactivity “tiers” and assess what percentage of tweets that reached each tier contained images. For simplicity’s sake, lets use Likes to measure interactivity, and separate tweets into four tiers:
tier_4— 2 or fewer likes
tier_3— 3-9 likes
tier_2— 10-15 likes
tier_1— 16+ likes
To accomplish this, we can use. We’ll give it two arguments: a list of our conditions, and a correspding list of the value we’d like to assign to each row in our new column.
This means that the order matters: if the first condition in our
conditions list is met, the first value in our
values list will be assigned to our new column for that row. If the second condition is met, the second value will be assigned, et cetera.
Let’s take a look at how this looks in Python code:
# create a list of our conditions conditions = [ (df['likes_count'] <= 2), (df['likes_count'] > 2) & (df['likes_count'] <= 9), (df['likes_count'] > 9) & (df['likes_count'] <= 15), (df['likes_count'] > 15) ] # create a list of the values we want to assign for each condition values = ['tier_4', 'tier_3', 'tier_2', 'tier_1'] # create a new column and use np.select to assign values to it using our lists as arguments df['tier'] = np.select(conditions, values) # display updated DataFrame df.head()
Awesome! We’ve created another new column that categorizes each tweet based on our (admittedly somewhat arbitrary) tier ranking system.
Now, we can use this to answer more questions about our data set. For example: what percentage of tier 1 and tier 4 tweets have images?
#tier 4 tweets df[(df['tier'] == 'tier_4')]['hasimage'].value_counts(normalize=True)
False 0.948784 True 0.051216 Name: hasimage, dtype: float64
#tier 1 tweets df[(df['tier'] == 'tier_1')]['hasimage'].value_counts(normalize=True)
False 0.836842 True 0.163158 Name: hasimage, dtype: float64
Here, we can see that while images seem to help, they don’t seem to be necessary for success. More than 83% of Dataquest’s “tier 1” tweets — the tweets with 15+ likes — had no image attached.
While this is a very superficial analysis, we’ve accomplished our true goal here: adding columns to pandas DataFrames based on conditional statements about values in our existing columns.
Of course, this is a task that can be accomplished in a wide variety of ways.
np.select() are just two of many potential approaches. If you’d like to learn more of this sort of thing, check out , and the other courses in the .
This article has been published from the source link without modifications to the text. Only the headline has been changed.