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Pandas is an open-source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. As one of the most popular data-wrangling packages, Pandas works well with many other data science modules inside the Python ecosystem and is typically included in every Python distribution. Here are the top 10 Python Pandas interview questions that will help you land a FAANG job soon.
A Series is defined as a one-dimensional array that is capable of storing various data types. The row labels of the series are called the index. By using a 'series' method, we can easily convert the list, tuple, and dictionary into a series. A Series cannot contain multiple columns.
The Pandas std() is defined as a function for calculating the standard deviation of the given set of numbers, DataFrame, columns, and rows.
A DataFrame is a widely used data structure for pandas. It works with a two-dimensional array with labeled axes (rows and columns) DataFrame is defined as a standard way to store data and has two different indexes, i.e., row index and column index.
Reindexing is used to conform DataFrame to a new index with optional filling logic. It places NA/NaN in that location where the values are not present in the previous index. It returns a new object unless the new index is produced as equivalent to the current one, and the value of copy becomes False. It is used to change the index of the rows and columns of the DataFrame.
Categorical data is defined as a Pandas data type that corresponds to a categorical variable in statistics. A categorical variable is generally used to take a limited and usually fixed number of possible values. Examples: gender, country affiliation, blood type, social class, observation time, or rating via Likert scales. All values of categorical data are either in categories or np.nan.
We can create a copy of the series by using the following syntax:
pandas.Series.copy
Series.copy(deep=True)
The above statements make a deep copy that includes a copy of the data and the indices. If we set the deep value to False, it will neither copy the index nor the data.
A DataFrame is a widely used data structure of pandas and works with a two-dimensional array with labeled axes (rows and columns) It is defined as a standard way to store data and has two different indexes, i.e., row index and column index.
Adding an Index to a DataFrame
Pandas allow adding the inputs to the index argument if you create a DataFrame. It will make sure that you have the desired index. If you don't specify inputs, the DataFrame contains, by default, a numerically valued index that starts with 0 and ends on the last row of the DataFrame.
You can use the. rename method to give different values to the columns or the index values of DataFrame.
You can iterate over the rows of the DataFrame by using for loop in combination with an iterrows() call on the DataFrame.
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