Python for Data Analysis

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682 omtaler

Intermediate

Først og fremst vil vi diskutere bruksområdene til NumPy og hvorfor det er populært. Deretter vil du lære ulike muligheter for å opprette forskjellige matriser i NumPy.

Det er nå på tide å lære hvordan du bruker indeksnotasjon for å hente spesifikke elementer eller delsett av data fra matriser. Du vil også lære hvordan du bruker indekser for å hente data basert på visse betingelser og håndtere manglende verdier.

Det er ganske mange vanlige operasjoner på matriser som du kanskje vil utføre. Heldigvis har NumPy innebygde funksjoner og metoder for dette formålet som du vil lære her.

Det er på tide å oppdage hvordan man effektivt bruker matematiske operasjoner på NumPy-arrays og anvender dem i noen virkelige problemer.

I denne delen skal vi utforske det grunnleggende i Series- og DataFrame-strukturer. Du vil også lære om forskjellene mellom disse to typene strukturer.

Data kan hentes i ulike formater, som CSV, JSON, SQL, HTML og mer. Med Pandas er du ikke begrenset til ett enkelt format — du kan arbeide med data fra en rekke filtyper. I dette kapittelet vil vi spesielt fokusere på CSV- og TXT-formatene.

Her vil du lære hvordan du behandler rådata ved å fjerne overflødig informasjon og håndtere nullverdier i et datasett.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events.

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

First, we will discuss plotting applications, Matplotlib library, and its advantages. Afterwards you will create your first simple plot using Matplotlib.

Now you are ready to create various commonly used in practice plots. Without further ado, let's dive in right now!

Plotting and visualization is not only about creating a plot or several plots, it is also about making them clear, understandable and pleasant to look at. Time to learn plots customization in maplotlib, let's do it!

Statistics is essential in working with data, hence why it is extremely important to know how to create various statistical plots. Now it's time for us to explore the most common ones.

Seaborn is another powerful plotting library in Python built on top of matplotlib. Now that you are familiar with matplotlib, it's time to dive into visualization with seaborn.