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# MachineLearningPlus

Machine Learning Plus is made of a group of enthusiastic folks passionate about Data Science. They help awesome Developers, Business managers and Data Scientists become better at what they do.

## List Comprehension

Now let’s understand list comprehensions this is a slightly more advanced concept compared to list a list comprehension. Now, remember earlier, I was giving you one to one problem and in order to solve that problem you need to understand list and list comprehension. We have now understood what list is let’s get to a …

## How to use Numpy Random Function in Python

How to use numpy.random.rand() function ? numpy.random.rand() function is used to generate random float values from an uniform distribution over [0,1). These values can be extracted as a single value or in arrays of any dimension. In this article, you will learn about various use cases of this function. Structural overview of numpy.random.rand() Syntax: numpy.random.rand(d0, …

## Numpy Reshape – How to reshape arrays and what does -1 mean?

How to reshape a numpy array? The numpy.reshape() function is used to reshape a numpy array without changing the data in the array. It is a very common practice to reshape arrays to make them compatible for further calculations.

## Pandas Sample – Randomly Sample Rows From Dataframe

Use the pandas.DataFrame.sample() method from pandas library to randomly select rows from a DataFrame Randomly selecting rows can be useful for inspecting the values of a DataFrame.

## Pandas Iterate Over Rows

Pandas DataFrame is used very popularly to store tabular data. Very often, we need to iterate over rows of the dataframe to perform various operations. This is a way of navigating the DatraFrame.

## Pandas Describe

How to use Pandas Describe function? The pandas.describe function is used to get a descriptive statistics summary of a given dataframe. This includes mean, count, std deviation, percentiles, and min-max values of all the features. On applying pandas describe function to a dataframe, the result is also returned as a dataframe . This dataframe will …

## Pandas Dataframe.duplicated()

The pandas.DataFrame.duplicated() method is used to find duplicate rows in a DataFrame. It returns a boolean series which identifies whether a row is duplicate or unique. In this article, you will learn how to use this method to identify the duplicate rows in a DataFrame. You will also get to know a few practical tips …

## Creating pandas series from dictionary

Pandas Series is a 1-dimensional array like object which can hold data of any type. You can create a pandas series from a dictionary by passing the dictionary to the command: pandas.Series(). In this article, you will learn about the different methods of configuring the pandas.Series() command to make a pandas series from a dictionary …

## Pandas Head – How to get the first few rows?

pandas.head() function is used to access the first n rows of a dataframe or series. It returns a smaller version of the caller object with the first few entries.

## RegEx Replace values using Pandas

RegEx (Regular Expression) is a special sequence of characters used to form a search pattern using a specialized syntax While working on data manipulation, especially textual data, you need to manipulate specific string patterns. These may include retrieving hashtags from a tweet, extracting dates from a text, or removing website links. Pandas replace() function is …

## Pandas Histogram

Let’s understand how to create histogram in pandas and how it is useful. Histograms are very useful in statistical analysis. Histograms are generally used to represent the frequency distribution for a numeric array, split into small equal-sized bins. As we used pandas to work with tabular data, it’s important to know how to work with …

## Pandas Select Columns

Let’s understand how to select columns in pandas using various useful method.

## Pandas Line Plot | Python

Pandas provides you a quick and easy way to visualize the relationship between the features of a dataframe. The Pandas line plot represents information as a series of data points connected with a straight line. Very often, we use this to find out how a particular feature changes with respect to time and also with …

## Pandas DateTime

Let’s understand how to work with Date-Time values in Pandas. While working with real time data, we often come across date or time values. The raw data itself might be represented as a string of text, but you will want to convert it to a datetime format in order to work with it. These are …

## Pandas read_csv() – How to read a csv file in Python

Most of the data is available in a tabular format of CSV files. It is very popular. You can convert them to a pandas DataFrame using the read_csv function. The pandas.read_csv is used to load a CSV file as a pandas dataframe.

Let’s see how to add a new columns to an existing Pandas Dataframe. Adding columns to a DataFrame is one of the most crucial operations you have to perform while working on a project. It is required for several reasons such as adding new data which is relevant to the problem you are trying to …

## Pandas Groupby Examples

Pandas Groupby operation is used to perform aggregating and summarization operations on multiple columns of a pandas DataFrame. These operations can be splitting the data, applying a function, combining the results, etc.

## Pandas Dateframe Index

Introduction In a dataframe, we have huge number of data records. There must be something unique for each data record so that we can access it distinctly. You can use the pandas dataframe index for this. They are referred as row names or index names also in general. By default, these row index labels are …

## Pandas Series to List

Pandas series can be converted to a list using tolist() or type casting method. There can be situations when you want to perform operations on a list instead of a pandas object. In such cases, you can store the DataFrame columns in a list and perform the required operations. After that, you can convert the …

## Pandas Dropna – How to drop missing values?

In reality, majority of the datasets collected contain missing values due to manual errors, unavailability of information, etc. Although there are different ways for handling missing values, sometimes you have no other option but to drop those rows from the dataset. A common method for dropping rows and columns is using the pandas dropna function.

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