Server Side Programming Articles - Page 726 of 2650

The popular approach of installing pandas is using Anaconda distributions. Anaconda provides pre-installed libraries and applications by default, and we no need to install any packages externally.However, this approach means we are installing two many packages by default, Due to this anaconda costs more system storage.If you want to have more control over package installation, or if you want to save your system storage then using Miniconda may be a better solution than using anaconda.One can choose Miniconda if that −Do not have any problem installing each of the packages individually, if they want to work on a particular package.Don't ... Read More

Anaconda is a distribution of packages built for data science. as we know that pandas is a python package that is the best tool for data science operations. Anaconda is a python and R distribution, and it includes 100 plus python packages by default. It is also flexible to use in Windows machines as well as Linux machines.When you download Anaconda it will automatically come with conda(package manager), Python, and over 150 python scientific packages. It also has some default applications like Jupyter Notebook, Spyder, RStudio, Visual Studio Code, and some more.To install Anaconda, we need to download the anaconda ... Read More

Python pandas package can be installed via multiple ways which are −Using Anaconda distributions Using mini conda Using pipUsing Anaconda distributionsIf you are using anaconda distribution already in your system then no need to install pandas again Because pandas is a part of anaconda distribution. So we can directly import the pandas.To install a specific pandas version, give the below commandconda install pandas=1.1.5This will install the pandas 1.1.5 versionUsing mini condaBoth Anaconda and minconda use the conda package installer, but using anaconda will occupy more system storage. Because anaconda has more than 100 packages, those are automatically installed and the ... Read More

Pandas is built on top of NumPy, which means the Python pandas package depends on the NumPy package and also pandas intended with many other 3rd party libraries. So we can say that Numpy is required for operating the Pandas.The pandas library depends heavily on the Numpy array for the implementation of pandas data objects.Exampleimport pandas as pd df = pd.DataFrame({'A':[1, 2, 3, 4], 'B':[5, 6, 7, 8]}) print('Type of DataFrame: ', type(df)) print('Type of single Column A: ', type(df['A'])) print('Type of values in column A', type(df['A'].values)) print(df['A'].values)Explanationdf variable stores a DataFrame object created by using python ... Read More

To fill missing values after merging the data frames with another value than NA in R, we can follow the below steps −First of all, create two data frames.Then, merge the data frames by a common column between the two.After that, replace the NAs with another value.ExampleCreate the first data frameLet’s create a data frame as shown below −ID

To use top_n function from dplyr to extract rows based on one column ordered indescending order in R, we can follow the below steps −First of all, create a data frame.Then, use top_n function dplyr package along with arrange and desc function to extract rows based on one column ordered in descending order.ExampleCreate the data frameLet’s create a data frame as shown below −x

To subset a matrix in R by specifying columns that contains NA, we can follow the below steps −First of all, create a matrix with some NAs.Then, use is.na along with subset function to subset the matrix by specifying columns that contains NA.ExampleCreate the matrixLet’s create a matrix as shown below −M

To create quantile regression plot with larger width of lines using ggplot2 in R, we can follow the below steps −First of all, create a data frame.Then, use stat_quantile function with size argument and geom_point function of ggplot2 package to create quantile regression plot.ExampleCreate the data frameLet’s create a data frame as shown below −x

To remove a row from matrix in R by using its name, we can follow the below steps −First of all, create a matrix.Then, add names to rows of the matrix.After that, subset the matrix by deselecting the desired row with negation and single square brackets for subsetting.ExampleCreate the matrixLet’s create a matrix as shown below −M

To remove a column from matrix in R by using its name, we can follow the below steps −First of all, create a matrixThen, add names to columns of the matrix.After that, subset the matrix by deselecting the desired column with negation and single square brackets for subsetting.ExampleCreate the matrixLet’s create a matrix as shown below −M