We do so by sorting by the ‘state’ column in each data frame, then resetting the index values in order starting at zero:Finally, we can merge the data. Another thing we can notice is the consistency between ACT participation rates from 2017 to 2018. In the context of movie reviews, it’s hard to intuitively make sense of how numbers would be useful. Let’s look at their frequencies:Hmm, it’s interesting to see ‘film’ is more frequent than ‘movie’ in positive reviews. Let’s quickly check:We will answer this question when we look at common tokens soon.It’s time to find out common n-grams. Let’s compare the column names among each of the data frames using the Since the goal of this analysis is to compare SAT and ACT data, the more similarly we can represent each dataset’s values, the more helpful our analysis will be. For example, some states require only the SAT, only the ACT, both of the exams, alternative standardized exams, or each student is required to take one standardized exam of their choosing. Hope you are feeling warmed up. Hopefully, this post gave you a taste of how to structure the analysis and showed example questions you could think about during the process. Using the Pandas data frame Success! Bigrams may be potentially useful but trigrams and fourgrams are not frequent enough relative to the token frequency.Let’s check the descriptive statistics of the variables of interest:We have the answers to the first four questions in this table. Let’s inspect some of them:Interesting, some are valid long words whereas some are long because they lack white space or outlaw words (i.e. This variability between standardized testing expectations, set by each state, should be considered a significant source of bias for exam records between states, such as participation rates and average performance. With this in mind, let’s tokenise the text into alphabetic tokens:Now we have tokenised, we can answer the first two questions:There are over 10 million tokens in the training data with around 122 thousand unique tokens. Let’s look at the exact counts for those longer than 10 characters:17+ characters long words are infrequent. longer run time).

Weaker relationships are represented by values that are closer to zero. Make learning your daily ritual. Negatively related variables, values with correlations between negative one and zero, indicate that one variable decreases as the other variable increases.

Now we can address the issue of inconsistent number of columns between the ACT datasets. Otherwise, this post will be over a few hours long. Exploratory Data Analysis – EDA – plays a critical role in understanding the what, why, and how of the problem statement.It’s first in the order of operations that a data analyst will perform when handed a new data source and problem statement. By conducting my own preliminary research, I discovered some evident issues with the SAT and ACT exams fairly quickly. how many values in the data fall within the range of 40%-50%). In fact, we will be doing a bit of data preparation in this post for the purpose of exploratory text analysis.
The example used in this tutorial is an exploratory analysis of historical SAT and ACT data to compare participation and performance between SAT and ACT exams in different States.

Long strings look quite interesting and there are a few key takeaways:There are other interesting findings we could add to these but these are good starters for now.It’s important to understand whether these cases that we just explored are prevalent enough to justify additional preprocessing steps (i.e.

In that case, it’s handy to keep Roughly less than 1% of strings contained hyphenated words. For this analysis, I examined and manipulated available CSV data files containing data about the SAT and ACT for both 2017 and 2018 in a Jupyter Notebook. When examining histograms and box plots, I’ll be focusing on visualizing the distribution of participation rates. From the histograms, we can notice that there are more States with 90%–100% participation rates for the ACT in 2017 and 2018. By using strong exploration of your data to guide outside research, you will be able to derive provable insights effectively and efficiently.Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. The list of stop words could extend in future. data-science exploratory-data-analysis data-analysis Updated Jun 26, 2020; Python; pyaf / DenseNet-MURA-PyTorch Star 51 Code Issues Pull requests Implementation of DenseNet model on Standford's MURA dataset using PyTorch. Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results. It appears that we could extend the stop words to include a few more. Finding answers to questions often leads to other questions that you may want to explore. I would say ‘Before we dive in, let’s take a step back and look at the bigger picture first. Now we can use Matplotlib and Seaborn to take a closer look at our clean and combined data frame.

Let’s use the masking technique to check which of the values ‘Washington, D.C.’ and ‘District of Columbia’ is in the ACT 2017 ‘State’ column:Now we officially have enough evidence to justify replacing the ‘Washington, D.C.’ value with ‘District of Columbia’ in the ACT 2018 data frame.
We’ll do so with a technique known as masking, which allows us to examine the rows within a data frame that meet specified criteria.

It’s unlikely that they will be useful as features, but we could always experiment.


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