Tag dataframe

A data frame is a standard way to store data and data is aligned in a tabular fashion in rows and columns.

DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index Let us assume that we are creating a data frame with student’s data, it will look something like this.

A pandas DataFrame can be created using the following constructor

pandas.DataFrame( data, index, columns, dtype, copy)

• Data –  data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.
• Index – For the row labels, the Index to be used for the resulting frame is Optional Default np.arrange(n) if no index is passed.
• Columns – For column labels, the optional default syntax is – np.arrange(n). This is only true if no index is passed.
• dtype – Data type of each column.
• Copy – This command (or whatever it is) is used for copying of data, if the default is False.

Creations of DataFrame:

A pandas DataFrame can be created using various inputs like list, dict, series, numpy ndarray, another dataframe.

Let’s explore DataFrame with python in jupyter notebook.

To do data analysis successfully, a Data analyst should know how to read and write different file format such as .CSV, .XLS, .HTML, JASON etc.

DataFrame has a Reader and a Writer function. The Reader function allows you to read the different data formats while the Writer function enables you to save the data in a particular format.

Below is a table containing available readers and writers.

Following notebook is the reference code for getting input and output, pandas can read a variety of file types using it’s pd.read_ methods. Let’s take a look at the most common data types:

Groupby basically split the data into different groups depending upon the criteria.

Groupby function can be use for following operations on the original object.
They are −

• Splitting the Object
• Applying a function
• Combining the results

Let’s understand groupby by following python code

There are various useful pandas operation available which is really handy in data analysis.

In this lecture we are going to cover following topics:

• How to find unique values.
• How to select data with multiple conditions?
• How to apply function on a particular column?
• How to remove column?
• How to get column and index name?
• Sorting by column
• Checking null value
• Filling in NaN values with something else
• Pivot table creation
• Change column name in pre-existing data frame.
• .map() and .apply() function
• Get column name in the data frame
• Change order of column in the data frame
• Add new column in existing data frame
• Data type conversion
• Date and time conversion

Let’s see all these operations in python