1680824580

In this article, you are going to learn about how to calculate Variance in Pandas DataFrame

Pandas is a Python library that is widely used to perform data analysis and machine learning tasks. It is open-source and very powerful, fast, and easy to use. Basically, while working with big data we need to analyze, manipulate and update them and the pandas’ library plays a lead role there. Sometimes, we need to calculate the variance in a Pandas DataFrame.

Variance is a statistical term that refers to the measurement of dispersion that calculates the spread of all data points in a data set. It helps us to measure how much the data are separated from each other. Pandas Provide a function named `var()`

to calculate the variance. In this article, we are going to explore this function and see how we can calculate Variance in Pandas DataFrame. Before doing so, let’s create a Pandas DataFrame first that consists of different data columns.

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
print(student_df)
# Output:
# Age CT_Marks1 CT_Marks2
# 0 20 71 81
# 1 21 83 91
# 2 24 64 74
# 3 23 61 81
# 4 19 83 73
```

Here, you can see that we have created a simple Pandas DataFrame that represents the student’s age, and CT marks. We will perform the variance based on this DataFrame’s information.

**Calculate Variance: Single Column**

We can calculate the Variance of a single column in Pandas DataFrame. The `var()`

function will return the result and we need to store it in a variable. Let’s calculate the variance of the `Age`

column in the below section:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
age_var = student_df['Age'].var()
print('Age Variance: ',age_var)
# Output:
# Age Variance: 4.3
```

Here, you can see that we have calculated the variance of a single column which is Age. The result of the Age column variance is 4.3.

**Calculate Variance: Multiple Columns**

We can also calculate the Variance in multiple columns. The process of performing this action is almost the same. All we need to do is to add the multiple columns’ names inside a list and separate them with a comma. See the below code example:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
multiple_cols_var = student_df[['CT_Marks1', 'CT_Marks2']].var()
print(multiple_cols_var)
# Output:
# CT_Marks1 106.8
# CT_Marks2 52.0
# dtype: float64
```

Here, you can see that we have calculated the variance for `CT_Marks1`

and `CT_Marks2`

and you can see the result in the output as 106.8 and 52.0. Here, we have shown two column variance but you can add as many columns as you want by following this process.

**Calculate Variance: Whole DataFrame**

We can also calculate the variance of a Pandas DataFrame as a whole. That means all the column’s variance. To perform this action see the below code example:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
df_var = student_df.var()
print(df_var)
# Output:
# Age 4.3
# CT_Marks1 106.8
# CT_Marks2 52.0
# dtype: float64
```

This is all about calculating the Variance in Pandas DataFrame. You may follow these approaches to calculate the Variance in Pandas DataFrame.

Original article source at: https://codesource.io/

1623927960

**Python** is famous for its vast selection of **libraries** and **resources** from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as **Numpy**, **Pandas**, **Scikit-learn**, **Keras**, and **TensorFlow**. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing **Big Data**, such as **Apache Spark**. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a comparison between the **Pandas DataFrame** and **Spark DataFrame**. My hope is to provide more conviction on choosing the right implementation.

**Pandas** has become very popular for its ease of use. It utilizes DataFrames to present data in **tabular** format like a spreadsheet with rows and columns. Importantly, it has very **intuitive methods** to perform common analytical tasks and a relatively **flat learning curve**. It loads all of the data into memory on a single machine (**one node**) for rapid execution. While the Pandas DataFrame has proven to be tremendously powerful in manipulating data, it does have its limits. With data growing at an exponentially rate, complex data processing becomes expensive to handle and causes performance degradation. These operations require **parallelization** and **distributed computing**, which the Pandas DataFrame does not support.

**Apache Spark** is an open-source **cluster computing** framework. With cluster computing, data processing is distributed and performed in parallel by **multiple nodes**. This is recognized as the **MapReduce** framework because the division of labor can usually be characterized by sets of the **map**, **shuffle**, and **reduce** operations found in **functional programming**. Spark’s implementation of cluster computing is unique because processes 1) are executed **in-memory** and 2) build up a query plan which does not execute until necessary (known as **lazy execution**). Although Spark’s cluster computing framework has a broad range of utility, we only look at the Spark DataFrame for the purpose of this article. Similar to those found in Pandas, the Spark DataFrame has intuitive **APIs**, making it easy to implement.

#pandas dataframe vs. spark dataframe: when parallel computing matters #pandas #pandas dataframe #pandas dataframe vs. spark dataframe #spark #when parallel computing matters

1623370500

Hey - Nick here! This page is a free excerpt from my $199 course Python for Finance, which is 50% off for the next 50 students.

If you want the full course, click here to sign up.

It’s now time for some practice problems! See below for details on how to proceed.

All of the code for this course’s practice problems can be found in this GitHub repository.

There are two options that you can use to complete the practice problems:

- Open them in your browser with a platform called Binder using this link (recommended)
- Download the repository to your local computer and open them in a Jupyter Notebook using Anaconda (a bit more tedious)

Note that binder can take up to a minute to load the repository, so please be patient.

Within that repository, there is a folder called `starter-files`

and a folder called `finished-files`

. You should open the appropriate practice problems within the `starter-files`

folder and only consult the corresponding file in the `finished-files`

folder if you get stuck.

The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.

#dataframes #pandas #practice problems: how to join dataframes in pandas #how to join dataframes in pandas #practice #/pandas/issues.

1624431580

In this tutorial, we are going to discuss different ways to add a new column to pandas data frame.

Table of Contents

**Pandas data frame**is a two-dimensional heterogeneous data structure that stores the data in a tabular form with labeled indexes i.e. rows and columns.

Usually, data frames are used when we have to deal with a large dataset, then we can simply see the summary of that large dataset by loading it into a pandas data frame and see the summary of the data frame.

In the real-world scenario, a pandas data frame is created by loading the datasets from an existing CSV file, Excel file, etc.

But pandas data frame can be also created from the list, dictionary, list of lists, list of dictionaries, dictionary of ndarray/lists, etc. Before we start discussing how to add a new column to an existing data frame we require a pandas data frame.

#pandas #dataframe #pandas dataframe #column #add a new column #how to add a new column to pandas dataframe

1586702221

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial

1680824580

In this article, you are going to learn about how to calculate Variance in Pandas DataFrame

Pandas is a Python library that is widely used to perform data analysis and machine learning tasks. It is open-source and very powerful, fast, and easy to use. Basically, while working with big data we need to analyze, manipulate and update them and the pandas’ library plays a lead role there. Sometimes, we need to calculate the variance in a Pandas DataFrame.

Variance is a statistical term that refers to the measurement of dispersion that calculates the spread of all data points in a data set. It helps us to measure how much the data are separated from each other. Pandas Provide a function named `var()`

to calculate the variance. In this article, we are going to explore this function and see how we can calculate Variance in Pandas DataFrame. Before doing so, let’s create a Pandas DataFrame first that consists of different data columns.

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
print(student_df)
# Output:
# Age CT_Marks1 CT_Marks2
# 0 20 71 81
# 1 21 83 91
# 2 24 64 74
# 3 23 61 81
# 4 19 83 73
```

Here, you can see that we have created a simple Pandas DataFrame that represents the student’s age, and CT marks. We will perform the variance based on this DataFrame’s information.

**Calculate Variance: Single Column**

We can calculate the Variance of a single column in Pandas DataFrame. The `var()`

function will return the result and we need to store it in a variable. Let’s calculate the variance of the `Age`

column in the below section:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
age_var = student_df['Age'].var()
print('Age Variance: ',age_var)
# Output:
# Age Variance: 4.3
```

Here, you can see that we have calculated the variance of a single column which is Age. The result of the Age column variance is 4.3.

**Calculate Variance: Multiple Columns**

We can also calculate the Variance in multiple columns. The process of performing this action is almost the same. All we need to do is to add the multiple columns’ names inside a list and separate them with a comma. See the below code example:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
multiple_cols_var = student_df[['CT_Marks1', 'CT_Marks2']].var()
print(multiple_cols_var)
# Output:
# CT_Marks1 106.8
# CT_Marks2 52.0
# dtype: float64
```

Here, you can see that we have calculated the variance for `CT_Marks1`

and `CT_Marks2`

and you can see the result in the output as 106.8 and 52.0. Here, we have shown two column variance but you can add as many columns as you want by following this process.

**Calculate Variance: Whole DataFrame**

We can also calculate the variance of a Pandas DataFrame as a whole. That means all the column’s variance. To perform this action see the below code example:

```
import pandas as pd
student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
'CT_Marks1' : [71, 83, 64, 61, 83],
'CT_Marks2' : [81, 91, 74, 81, 73],
})
df_var = student_df.var()
print(df_var)
# Output:
# Age 4.3
# CT_Marks1 106.8
# CT_Marks2 52.0
# dtype: float64
```

This is all about calculating the Variance in Pandas DataFrame. You may follow these approaches to calculate the Variance in Pandas DataFrame.

Original article source at: https://codesource.io/