Crazy B*tch

What's the sexiest job in the 21st century? * Data Scientist! __Coffee Break Pandas__ teaches you the new superpower of analyzing and processing data with Python's Pandas framework. If solving puzzles is fu __for you, you'll love ❤ this book with 74 brand-new, hand-crafted Pandas puzzles to__ help you stay relevant in today's marketplace. * Data is the new oil -- whoever develops the skills to harvest this largely untapped asset class will increase their value in the 21st-century marketplace. * Computers and sensors create ever-growing data sets that become increasingly relevant in every part of our lives. * Smartphones collect billions of GPS traces that reveal valuable information towards new mobility solutions and truly smart cities. * Smartwatches track the heart rates of millions of people to detect health problems and prevent unnecessary deaths. * Intelligent cars collect myriads of sample data points that increase traffic efficiency, prevent millions of car accidents, and save humanity hundreds of millions of hours in traffic jams. Due to __Moore's Law__ -- __the number of transistors on a microchip doubles roughly every two years__ -- it became financially viable to store, process, and analyze hundreds of terabytes of data. The combination of increasing amounts of structured data and our improved capability to extract value from this data will result in explosive growth of the data science opportunity. Pandas is exceptionally suited to analyze structured data in an easy-to-understand and computationally efficient way. This unique Pandas learning book builds on psychological research to teach 74 Pandas concepts in an easy, fun, and educational way. ✓ Learn faster based on small feedback loops : (I) read puzzle, (II) guess your solution, and (III) compare it to the gold standard and study the explanation. ✓ Track your coding skill level and discover your Pandas rank compared to others -- from Absolute Beginner to Professional to Grandmaster. ✓ Master the Pandas framework and start your career as a data scientist. Contents Introduction A Case for Puzzle-based Learning Overcome the Knowledge Gap Embrace the Eureka Moment Divide and Conquer Improve From Immediate Feedback Measure Your Skills Individualized Learning Small is Beautiful Active Beats Passive Learning Make Code a First-class Citizen What You See is All There is Elo Rating Python How to Use This Book How to Test and Train Your Skills? Pandas Code Puzzles Elo 1500-1600 Create a Series From a Scalar Value Create a Series From a List of Data Create Series with Custom Index Values Create a Series From a Dictionary The Dimension of a Series The Size of a Series Checking Series for NaN-Values Integer Location Index Location Index Methods of Series: all() and any() The Methods min() and max() Update Values in a Series Index Operators I Index Operators II Filtering Series Method Chaining The axis Argument Working with Multiple Series Create a DataFrame from a List of Lists Create a DataFrame from a Dictionary of Lists List of Dict to DataFrame Create a DataFrame from a List of Tuples Create a DataFrame from Series Create a DataFrame from a CSV File Pandas Code Puzzles Elo 1600-1700 DataFrame Head DataFrame Tail Slices of DataFrames I Slices of DataFrames II 2D Slicing DataFrame vs. Series Modifying Column Labels Sorting a DataFrame by Column Replacing Values Renaming Columns Column Datatypes Integer Location Index: Multiple Values Count Non-NaN Values Drop NaN-Values Adding Columns I Adding Columns II Boolean Indexing I Drop NaN-Values II Drop Columns Drop Selected Values Sort In-Place Reverse Column Order Reverse Row Order Reset the Index Reference Confusion Select Values From a List Boolean Indexing II Aggregation DataFrame Concatenation I DataFrame Concatenation II Inner Merge Right Merge Outer Merge Pandas Code Puzzles Elo 1700-1800 Fun With NaN DataFrame Information DataFrame Statistics DataFrame Memory Usage Numpy Arrays Regexing Column Labels Replacing NaN-Values Dummy Values Method Chaining II Length vs. Count Modifying Values Value Clusters Exploding Values Comparison: equals() vs. == Merged DataFrames Value Sources Pandas Code Puzzles Elo 1800+ Index iloc and Lambdas Pivot Tables Final Remarks Your Skill Level Where to Go From Here?