Why is the Empirical Distribution based on the Cumulative Distribution? Why is the Empirical Probability Distribution Function  based on the Cumulative Probability Distribution Function?
I have always seen the Empirical Distribution Function to have a "staircase" style shape, similar to a cumulative probability distribution function:

Is there any reason that the Empirical Distribution Function always appears in the shape of a "staircase" and estimates the Cumulative Probability Distribution Function instead of the Non-Cumulative Probability Distribution Function? Or is this a trivial matter - can the Empirical Probability Distribution be easily transformed into a Non-Cumulative Probability Distribution Function?
Can someone please explain this?
Thanks!
Note: Apparently F-hat (t) is an unbiased estimator of the true Cumulative Probability Distribution function F(t).
References:

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*https://en.wikipedia.org/wiki/Empirical_distribution_function
 A: The empirical distribution function $\hat{F}(\cdot)$

*

*is a step function by construction, since it puts a probability (Dirac) mass of $1/n$ on every term in the sample, $(x_1,\ldots,x_n)$, hence jumps by the same factor $1/n$ from one observation to the next. As a result, it is not everywhere differentiable and cannot be associated with a probability density function, meaning there is no density equivalent to the empirical distribution function (at least wrt a continuous measure like the Lebesgue measure).


*is "empirical" in the sense that it is based on an iid sample, $(x_1,\ldots,x_n)$, as opposed to the true cumulative distribution function, $F(\cdot)$, of the sample


*is a proper cumulative distribution function (cdf), corresponding to an average of Dirac distributions over the sample, $(x_1,\ldots,x_n)$


*estimates the true cdf $F$ with the property of being unbiased that$$\mathbb{E}^F[\hat{F}(x)]=F(x)$$for all $x$'s, and


*is converging a.s. to the true cdf $F$ for the uniform convergence norm. This is the Glivenko-Cantelli Theorem.
What is a non-cumulative distribution function?
A: The distribution function is a 'function to describe the distribution'.
But several functions can be used to describe a distribution, so the 'distribution function' may refer to different things. See for instance: Are the terms probability density function and probability distribution (or just "distribution") interchangeable?
Mostly used is the cumulative distribution function (CDF) because it uniquely defines a distribution, and it may be considered the density function. (I believe that the characteristic function and the cumulant generating function are also sometimes referred to with the term 'the distribution function').
According to this list of earliest uses of statistical terms the term 'distribution function' first occurred in 1919 in the German language literature (R. von Mises' "Grundlagen der Wahrscheinlichkeitsrechnung") and in 1935 in English language literature ( J. L. Doob's "The Limiting Distributions of Certain Statistics"). There is some works in English from 1933 by Aurel Witner, for instance "On the Stable Distribution Laws".
In those works by von Mises, Doob and Witner, the 'distribution function' is defined as what we know more commonly now as the cumulative distribution function. But there are around that time other uses of 'distribution function'. For instance in Nordic literature (more precise the Scandinavian Actuarial Journal) the term 'verteilungfunktion' occurs in 1919 as the probability density or frequency distribution, see Hongström and Hagström . We also see Wishart and Bartlett use the term 'distribution function' in 1933 referring to the probability density function "The generalised product moment distribution in a normal system" and Wilks in 1932.
Empirical distribution
So the 'empirical distribution' refers to an empirical estimate of the cumulative distribution function.
Below you see an example with a sample from a standard normal distribution.

Empirical frequency distribution
If the observations are discrete then instead of the probabilities $P(X \leq x)$ we could also describe the probabilities $P(X = x)$. This is also called the probability mass function(PMF).
Below is an example with the data from 'illustration I' in Pearson's article on the chi-squared statistic to test the goodness of fit for frequency curves.

The following data are due to Professor W.F.R. Weldon, F.R.S., and give the observed frequency of dice with 5 or 6 points when a cast of twelve dice was made 26 306 times:


Empirical density distribution

why doesn't the empirical distribution look like a "bell curve"

The bell curve is a probability density function (PDF). It is a density of the probability mass. The density function does not express probabilities, like the above $P(X \leq x)$ and $P(X = x)$. So we can not estimate the density function empirically by observing frequencies in a sample.
However, what sometimes is done is bin the data and create a histogram like the PMF case above. Other ways are estimating the density by some smoothening of the observed data.
Below is an example of estimating the normal distribution PDF with a kernel smoother. The sampled points are illustrated in the image as points at the top.

