Pandas from Basic to Advance For Absolute Beginners.

Learn to create a pandas series from a Python list with an optional custom index and name, and note that pandas assigns a default index, while numpy is often imported.

Convert a Python dictionary into a pandas series with pd.Series; pandas automatically uses dictionary keys as the series index.

Explore pandas series attributes such as size, data type, name, is unique, index, and values, and see how these properties support data analysis with examples.

Learn to import CSV files with pandas read_csv, convert a single-column data frame to a series using squeeze, and set the index column (season) for multi-column data.

Explore essential pandas series methods: head, tail, and sample on a 2000-row customer dataset imported with read_csv, showing first and last rows and random row selection.

Understand how value count in pandas reveals the frequency of each element in a series, with practical examples like profession distributions.

Learn to use pandas sort_values to sort profession counts and find the maximum by sorting in descending order, using ascending=False.

Learn to sort pandas data by index using sort_index and by value with sort_values, including ascending control and index-based ordering. The lesson also introduces value_counts and five essential Series methods.

Explore series maths methods in pandas, including count, sum, product, mean, median, mode, standard deviation, variance, min, max, and describe, illustrated with a video views dataset.

Master index access in a pandas series by using zero-based and negative indexing to retrieve season scores, such as first season (index 0) and last second season (index -2).

Explore slicing in pandas series to compute the sums of scores for the first and last five seasons, using index ranges and negative indexing to extract data and aggregate it.

Learn to use fancy indexing in pandas series to select specific seasons, sum scores, and fetch elements with a custom index through indexing and slicing.

Edit a pandas series by indexing and slicing to set or add values, handle new indices, and use fancy indexing to update multiple positions.

Explore Pandas series functionalities by inspecting length, type, sorting, and max and min values on a three-subject marks series, and review the full set of methods and attributes to explore.

Learn how to convert a Pandas series to common data structures by turning a series into a list, then into a dictionary, with practical type checks.

Explain how the membership operator works in a Pandas series, showing it tests the index by default and returns true for 'English', while value checks require explicit values.

Demonstrate looping in a pandas series by iterating with a for loop and printing values, showing the sequence 98, 87, 96, 98, 87, 96, and confirming looping works.

Explore arithmetic and relational operations in series with pandas, including adding 100 and filtering by greater than or not equal to 95 to select English and science results.

Learn to plot graphs on a pandas series using plot, creating dot plots, bar charts, and pie charts to visualize season-by-season goal data.

Convert a pandas series data type using astype to switch from float64 to float32, storing results in a new marks variable and printing to show the importance of type.

Learn to filter a Pandas series by using the between method to identify marks between 90 and 100, noting that it operates on values, not indices.

Learn how the clip method in a pandas series clamps values to a lower bound and an upper bound, as 92 stays between them, 88 becomes 90, and NaN appears.

Master how to remove duplicate values in a Pandas Series using drop_duplicates, eliminating repeated entries and keeping only unique elements for clean data.

Learn to detect null values in a pandas series using is null, identify their positions, and clean them by dropping or filling NaN with a chosen value.

Explore applying a lambda function to a pandas series to classify scores as good if above 90, else work on it, including converting to float.

Learn how to copy a Pandas Series with the dot copy method, create an independent copy, and modify the copy without altering the original series.

Learn how to create a pandas data frame from a Python list by combining series, and set columns id, name, and marks with a student data example.

Create a data frame from a Python dictionary by passing the student information dictionary to pd.DataFrame, reproducing the same result as with a list.

Learn to read a csv file into a data frame with read_csv, using movies.csv, store the result in a variable for future use, and print it to verify.

Explore core Pandas DataFrame concepts by examining shape, dtype, values, index, and columns; use head, tail, sample, info, and describe, and apply isnull and duplicated checks, then rename in place.

Discover axis arguments in pandas, performing column-wise sums with axis=0 and row-wise means with axis=1, via practical dataframe examples.

Learn to select rows from a data frame with iloc by index position and with loc by index label, using start, stop, and step, plus fancy index selection.

Learn to select rows with the loc function in pandas. Compare behavior when using the default index versus a custom index, and understand start, stop, and step.

Learn to select rows and columns in a pandas data frame using iloc, including start-stop range and optional steps, with integer indexing and handling string vs integer indexes.

Learn to add columns to a data frame in pandas, including constant columns, columns from a list, and derived scores using apply with a lambda.

Explore how to use group by in pandas to form groups from categorical columns and apply aggregate functions such as sum, max, and count across numerical data.

Apply group by techniques to an IMDb dataset to rank genres by gross, find the top average ratings, and count movies per actor, using sum, mean, and group-first/last insights.

Demonstrates pandas group by workflows, including get_group to fetch rows by genre, shows groups as a genre-to-indices map, and uses sample and unique to select and count within groups.

Master merging and joining in pandas, including inner, left, right, and full outer joins on student, enrollment, and courses dataframes to map which students enroll in which courses.

Learn how left join works in Pandas, contrasting it with inner join, to ensure all rows from the left table appear in the result, even without matches.

Learn how to perform a right outer join in pandas using merge with how='right', joining the enrollment table to the student table on student_id, ensuring all right rows appear.

Learn the full outer join in pandas, a union of left and right joins that includes all rows from student and enrollment tables, with nulls where there is no match.

Merge enrollment with courses on course id to calculate total revenue, compute per-course revenue with group by, and identify which course has maximum enrollment and which students didn't enroll.