What you will learn in Pandas Workout: 200 exercises to make you a stronger data analyst

200 exercises to make you a stronger data analyst

by Reuven M. Lerner

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Chapter by chapter

Chapter 1: Series

1.2 Exercise 1 • Test scores · 1.3 Working it out · 1.4 Solution · 1.5 Beyond the exercise · 1.6 Exercise 2 • Scaling test scores · 1.7 Working it out · 1.8 Solution · 1.9 Beyond the exercise · 1.10 Exercise 3 • Counting tens digits · 1.11 Working it out · 1.12 Solution · 1.13 Beyond the exercise · 1.14 Exercise 4 • Descriptive statistics · 1.15 Working it out · 1.16 Solution · 1.17 Beyond the exercise · 1.18 Exercise 5 • Monday temperatures · 1.19 Working it out · 1.20 Solution · 1.21 Beyond the exercise · 1.22 Exercise 6 • Passenger frequency · 1.23 Working it out · 1.24 Solution · 1.25 Beyond the exercise · 1.26 Exercise 7 • Long, medium, and short taxi rides · 1.27 Working it out · 1.28 Solution · 1.29 Beyond the exercise · 1.30 Summary

What you learn in chapter 1 →

Chapter 2: Data frames

2.1 Exercise 8 • Net revenue · 2.2 Working it out · 2.3 Solution · 2.4 Beyond the exercise · 2.5 Exercise 9 • Tax planning · 2.6 Working it out · 2.7 Solution · 2.8 Beyond the exercise · 2.9 Exercise 10 • Adding new products · 2.10 Working it out · 2.11 Solution · 2.12 Beyond the exercise · 2.13 Exercise 11 • Bestsellers · 2.14 Working it out · 2.15 Solution · 2.16 Beyond the exercise · 2.17 Exercise 12 • Finding outliers · 2.18 Working it out · 2.19 Solution · 2.20 Beyond the exercise · 2.21 Exercise 13 • Interpolation · 2.22 Working it out · 2.23 Solution · 2.24 Beyond the exercise · 2.25 Exercise 14 • Selective updating · 2.26 Working it out · 2.27 Solution · 2.28 Beyond the exercise · 2.29 Summary

What you learn in chapter 2 →

Chapter 3: Importing and exporting data

3.1 Exercise 15 • Weird taxi rides · 3.2 Working it out · 3.3 Solution · 3.4 Beyond the exercise · 3.5 Exercise 16 • Pandemic taxis · 3.6 Working it out · 3.7 Solution · 3.8 Beyond the exercise · 3.9 Exercise 17 • Setting column types · 3.10 Working it out · 3.11 Beyond the exercise · 3.12 Exercise 18 • passwd to df · 3.13 Working it out · 3.14 Solution · 3.15 Beyond the exercise · 3.16 Exercise 19 • Bitcoin values · 3.17 Working it out · 3.18 Solution · 3.19 Beyond the exercise · 3.20 Exercise 20 • Big cities · 3.21 Working it out · 3.22 Solution · 3.23 Beyond the exercise · 3.24 Summary

What you learn in chapter 3 →

Chapter 4: Indexes

4.1 Exercise 21 • Parking tickets · 4.2 Working it out · 4.3 Solution · 4.4 Beyond the exercise · 4.5 Exercise 22 • State SAT scores · 4.6 Working it out · 4.7 Solution · 4.8 Beyond the exercise · 4.9 Exercise 23 • Olympic games · 4.10 Working it out · 4.11 Solution · 4.12 Beyond the exercise · 4.13 Exercise 24 • Olympic pivots · 4.14 Working it out · 4.15 Solution · 4.16 Beyond the exercise · 4.17 Summary

What you learn in chapter 4 →

Chapter 5: Cleaning data

5.1 Exercise 25 • Parking cleanup · 5.2 Working it out · 5.3 Solution · 5.4 Beyond the exercise · 5.5 Exercise 26 • Celebrity deaths · 5.6 Working it out · 5.7 Solution · 5.8 Beyond the exercise · 5.9 Exercise 27 • Titanic interpolation · 5.10 Working it out · 5.11 Solution · 5.12 Beyond the exercise · 5.13 Exercise 28 • Inconsistent data · 5.14 Working it out · 5.15 Solution · 5.16 Beyond the exercise · 5.17 Summary

What you learn in chapter 5 →

Chapter 6: Grouping, joining, and sorting

6.1 Exercise 29 • Longest taxi rides · 6.2 Working it out · 6.3 Solution · 6.4 Beyond the exercise · 6.5 Exercise 30 • Taxi ride comparison · 6.6 Working it out · 6.7 Solution · 6.8 Beyond the exercise · 6.9 Exercise 31 • Tourist spending per country · 6.10 Working it out · 6.11 Solution · 6.12 Beyond the exercise · 6.13 Summary

What you learn in chapter 6 →

Chapter 7: Advanced grouping, joining, and sorting

7.1 Exercise 32 • Multicity temperatures · 7.2 Working it out · 7.3 Solution · 7.4 Beyond the exercise · 7.5 Exercise 33 • SAT scores, revisited · 7.6 Working it out · 7.7 Solution · 7.8 Beyond the exercise · 7.9 Exercise 34 • Snowy, rainy cities · 7.10 Working it out · 7.11 Solution · 7.12 Beyond the exercise · 7.13 Exercise 35 • Wine scores and tourism spending · 7.14 Working it out · 7.15 Solution · 7.16 Beyond the exercise · 7.17 Summary

What you learn in chapter 7 →

Chapter 8: Midway project

8.1 Problem · 8.2 Working it out · 8.3 Solution · 8.4 Summary

What you learn in chapter 8 →

Chapter 9: Strings

9.1 Exercise 36 • Analyzing Alice · 9.2 Working it out · 9.3 Solution · 9.4 Beyond the exercise · 9.5 Exercise 37 • Wine words · 9.6 Working it out · 9.7 Solution · 9.8 Beyond the exercise · 9.9 Exercise 38 • Programmer salaries · 9.10 Working it out · 9.11 Solution · 9.12 Beyond the exercise · 9.13 Summary

What you learn in chapter 9 →

Chapter 10: Dates and times

10.1 Exercise 39 • Short, medium, and long taxi rides · 10.2 Working it out · 10.3 Solution · 10.4 Beyond the exercise · 10.5 Exercise 40 • Writing dates, reading dates · 10.6 Working it out · 10.7 Solution · 10.8 Beyond the exercise · 10.9 Exercise 41 • Oil prices · 10.10 Working it out · 10.11 Solution · 10.12 Beyond the exercise · 10.13 Exercise 42 • Best tippers · 10.14 Working it out · 10.15 Solution · 10.16 Beyond the exercise · 10.17 Summary

What you learn in chapter 10 →

Chapter 11: Visualization

11.1 Exercise 43 • Cities · 11.2 Working it out · 11.4 Beyond the exercise · 11.5 Exercise 44 • Boxplotting weather · 11.6 Working it out · 11.8 Beyond the exercise · 11.9 Exercise 45 • Taxi fare breakdown · 11.10 Working it out · 11.12 Beyond the exercise · 11.13 Exercise 46 • Cars, oil, and ice cream · 11.14 Working it out · 11.16 Beyond the exercise · 11.17 Exercise 47 • Seaborn taxi plots · 11.18 Working it out · 11.20 Beyond the exercise · 11.21 Summary

What you learn in chapter 11 →

Chapter 12: Performance

12.1 Exercise 48 • Categories · 12.2 Working it out · 12.4 Beyond the exercise · 12.5 Exercise 49 • Faster reading and writing · 12.6 Working it out · 12.8 Beyond the exercise · 12.9 Exercise 50 • “query” and “eval” · 12.10 Working it out · 12.12 Beyond the exercise · 12.13 Summary

What you learn in chapter 12 →

Chapter 13: Final project

13.1 Problem · 13.2 Column names and meanings · 13.3 Working it out · 13.4 Summary

What you learn in chapter 13 →

Concepts explained in this book

data frameA pandas DataFrame is a two-dimensional labeled tabular data structure organized into rows and columns.
loc accessorThe pandas .loc accessor selects rows and columns by labels or label-based slices, including Boolean masks.
pandas indexA pandas Index is the axis-label structure that identifies and supports retrieval and alignment of values in Series and DataFrames.
read_csvread_csv is a pandas function that parses a CSV file or compatible data source into a DataFrame.
joinJoining combines rows from separate tabular data structures by matching values in a common key or index.
SeriesA pandas Series is a one-dimensional labeled array that can hold values of a common or compatible data type.
meanThe arithmetic average of a collection of numerical values, also serving as the central-location parameter of a normal distribution.
scatter plotA scatter plot displays individual observations as points positioned according to two numerical variables.
countcount is a pandas operation that returns the number of non-missing values in an object or along an axis.
data groupinggroupby is a pandas operation that partitions data according to one or more keys so that grouped computations can be performed.
boolean seriesA boolean series is a one-dimensional sequence whose elements are Boolean values.
nanNaN is a floating-point marker representing a missing or undefined numeric value in pandas and NumPy.
dropnadropna is a pandas method that removes missing values or rows and columns containing them according to specified criteria.
correlationA statistical measure of the strength and direction of association between the number of wines submitted by a country and its mean score.
multi-column indexA pandas MultiIndex is a hierarchical index formed from multiple levels of labels.
histogramA chart that displays the distribution of numeric values by grouping them into intervals and showing each group's frequency.
isinisin is a pandas method that tests whether each value belongs to a specified collection and returns Boolean results.
value_countsA pandas method that counts the occurrences of each distinct value in a one-dimensional labeled data structure.
CSVA delimited text file containing tabular data, typically encoded using the CSV format.
pivot tableA pivot table is a tabular summary that organizes values by categories in rows and columns and aggregates values at their intersections.
pandasAn open-source Python library for data analysis and manipulation, including labeled tabular data structures.
timestampA timestamp object represents a specific date and time as a structured value supporting temporal calculations and comparisons.
quantilesA quantile is a value that divides an ordered dataset so that a specified proportion of observations falls at or below it.
boolean indexingBoolean-mask indexing selects data elements at positions where a Boolean condition is true.
describeA pandas method that computes descriptive statistics for Series or DataFrame data.
str.containsA pandas string-accessor method that tests each string value against a substring or regular expression and returns Boolean results.
cutA pandas function that divides numeric values into intervals and optionally assigns labels to those intervals.
sort_valuessort_values is a pandas method that returns an object ordered by the values of one or more columns or other labeled data elements.
applyapply is a pandas method that applies a function to values or along an axis of a Series or DataFrame.
corrcorr is a pandas DataFrame method that computes pairwise correlations among numeric columns.
ilocThe pandas iloc accessor selects Series or DataFrame elements by integer position or position-based slices.
object data typeAn object dtype is a pandas or NumPy-compatible data type used for values stored as generic Python objects, often strings.
sorting algorithmSorting orders tabular records according to the values of an index or one or more columns.
parse_dates parameterparse_dates is a pandas read_csv parameter that identifies input columns whose values should be parsed as dates or datetimes.
grouped dataframeA pandas DataFrameGroupBy is the pandas object representing a DataFrame partitioned into groups and providing grouped operations.
box-and-whisker plotA boxplot, or box-and-whisker plot, graphically summarizes a numeric distribution using quartiles, a median, whiskers, and possible outliers.
set_indexThe pandas method that replaces an object's index with values from one or more specified columns or arrays.
MultiIndex.from_tuplesMultiIndex.from_tuples is a pandas constructor that creates a hierarchical index from a sequence of tuples.
pie chartA pie chart displays parts of a whole as sectors whose angular sizes represent category proportions.
pandas datetime accessorA pandas accessor that exposes datetime properties and operations for datetime-like Series and Index objects.
bar chartA bar plot represents categorical values with rectangular bars whose lengths are proportional to the represented quantities.
pandas.evalpandas eval is an expression-evaluation API that computes conditions or assignments involving pandas objects and their columns.
transpositiontranspose is a pandas operation that exchanges the rows and columns of a Series or DataFrame.
query methodA pandas DataFrame method that filters rows by evaluating a Boolean expression against the DataFrame's columns.
astypeA pandas or NumPy method that returns values cast to a specified data type.
rsplit methodstr.rsplit is a Python string method that splits a string from the right using a specified separator and maximum number of splits.
timeittimeit is a Python standard-library module for measuring the execution time of small code snippets.
drop_duplicatesA pandas method that removes duplicate rows or values according to the selected subset and retention rules.
time seriesA time series is a pandas data structure whose index contains timestamp values ordered or organized by time.
standard deviationA measure of the spread of numerical values around their mean, and a scale parameter of a normal distribution.
slice objectA slice is Python sequence syntax or an object specifying a range of positional elements to retrieve.
medianThe median is the middle value of an ordered set of numerical values, with averaging of the two middle values when the set has even size.
seabornSeaborn is a Python statistical data-visualization library that provides a high-level interface for drawing attractive and informative graphics.
explodeA pandas operation that expands list-like values in a Series or DataFrame into separate rows while retaining the original index labels.
stringAn immutable Python sequence of Unicode characters.
missing valueA missing or null value is an absent or unavailable observation or field in a dataset.
assignThe pandas DataFrame.assign method returns a new DataFrame with columns created or replaced from keyword arguments without modifying the original DataFrame.
pandas.DataFrame.resample()A pandas operation that groups time-indexed data into regular temporal intervals and applies aggregation or other computations to each interval.
MapA mutable Python mapping from unique keys to values.
column selectorA selection specification that identifies which data-frame columns an indexer should return.
line plotA chart that represents observations as points connected by line segments, typically to show change across an ordered dimension.
fillnaA pandas method that replaces missing values with a specified value or computed replacement.
memory usageMemory usage is the amount of computer memory occupied by a data structure or computation.
timedeltaA timedelta is a duration value representing a difference between dates or times and supporting temporal arithmetic.
MatplotlibMatplotlib is a Python library for creating static, animated, and interactive data visualizations.
str.isdigitstr.isdigit is a string-accessor method that returns whether each string consists solely of digit characters.
data cleaningData cleaning is the process of detecting, correcting, removing, or transforming inaccurate, incomplete, or inconsistent data.
catplotcatplot is a Seaborn figure-level function for visualizing relationships involving categorical variables.
relplotrelplot is a Seaborn function for visualizing relationships between variables, including numeric variables with scatter or line plots.
pandas.concatA pandas function that concatenates pandas objects along a specified axis and returns a combined object.
str.lowerA pandas string-accessor method that converts each string value in a Series to lowercase.
trimA pandas string-accessor method that removes specified characters from the beginning and end of each string.
pct_changepct_change is a pandas method that computes fractional or percentage changes between successive values along an axis.
regular expressionA regular expression is a pattern notation for describing and matching sets of text strings.
to_datetimepd.to_datetime is a pandas function that converts date and time representations into Timestamp values or datetime-like Series.
FeatherFeather is a binary columnar file format for efficiently storing and exchanging tabular data.
PyArrowPyArrow is a Python library providing access to Apache Arrow columnar data structures, memory formats, and related tools.
dummy indicatorA dummy column is a binary indicator column representing whether each record belongs to a particular category.
%timeit magicA timeit magic command is a Jupyter/IPython command that repeatedly executes code and reports its execution time.
resamplingResampling is the process of changing the temporal frequency of time-indexed data, commonly by aggregating observations into new time periods.
interquartile rangeThe interquartile range is the difference between the 75th-percentile and 25th-percentile values of an ordered dataset.
broadcastingBroadcasting is the automatic expansion or alignment of compatible operands so an operation can combine data with different shapes or scalar values.
normal distributionA probability distribution whose values are symmetrically concentrated around a mean with spread determined by its standard deviation.
varianceThe average of squared deviations from the mean before taking a square root.
isnullisnull is a pandas method that returns Boolean values indicating whether each Series or DataFrame value is null or missing.
floating-point arithmeticFloating-point arithmetic performs numerical calculations using finite-precision representations that can produce small rounding discrepancies.
anomalyAn outlier is an observation unusually far below or above the central range of a dataset according to a chosen criterion.
str.get_dummiesA pandas string-accessor operation that converts delimiter-separated categorical values into binary indicator columns.
SettingWithCopyWarningSettingWithCopyWarning is a pandas warning indicating that an assignment may be modifying a view or copied subset rather than the intended original object.
five-number summaryA five-number summary consists of the minimum, first quartile, median, third quartile, and maximum of a numeric distribution.
plot.boxplot.box is a pandas plotting method that creates box plots from Series or DataFrame data.
columnA column is a labeled vertical sequence of values in a pandas data frame, represented as a Series.
tail methodThe pandas tail method returns the last specified number of elements or rows from an object.
stacked bar chartA bar plot in which multiple component values are stacked within each bar to show a total and its parts.
tuple unpackingTuple unpacking assigns the elements of an iterable to multiple variables in one assignment.
scatter matrix plotA scatter matrix is a grid of scatter plots showing pairwise relationships among multiple numeric variables, typically with univariate plots on the diagonal.
fancy indexingFancy indexing selects multiple positions or labels by supplying an index collection rather than a single index.
idxmaxidxmax is a pandas operation that returns the index label associated with the maximum value.
default_rngdefault_rng is a NumPy function that constructs a modern pseudorandom number generator.
integersintegers is a NumPy random-generator method that returns an array of pseudorandom integers in a specified range and size.
NumPy arrayA NumPy array is a fixed-type multidimensional numerical array provided by NumPy.
NumPyNumPy is a Python library providing multidimensional arrays and numerical operations.
random seedA random seed is an initial value that determines the starting state of a pseudorandom-number generator.
scalarA scalar is a single numeric or other atomic value, as opposed to an array or labeled collection.
day_of_week attributeThe pandas datetime attribute that returns the weekday as an integer from Monday zero through Sunday six.
delimiterA field separator is a character or string that delimits adjacent fields in a structured text record.
str.sliceSeries.str.slice is a pandas string operation that extracts character ranges from each string value in a Series.
causationCausation is a relationship in which a change in one factor produces a change in another factor.
CategoricalDtypeCategoricalDtype is a pandas type specification that defines the categories and ordering for categorical data.
sort_indexsort_index is a pandas method that returns an object whose rows or elements are ordered by index values.
read_excelread_excel is a pandas function that reads spreadsheet files into a DataFrame.
plot.barA pandas plotting method that creates a bar plot from tabular or grouped data.
plot.histA pandas plotting method that creates a histogram from numeric data.
threshThe pandas thresh parameter specifies the minimum number of non-missing values required for an object or row to be retained.
float64float64 is a 64-bit floating-point data type commonly used as the default numeric precision for pandas columns.
infoThe pandas DataFrame info method displays concise information about the object's index, columns, dtypes, null counts, and memory usage.
inner joinA join operation that retains only index or key values present in every input table.
reset_indexreset_index is a pandas method that replaces an object's index with a default integer index and can optionally preserve the old index as a column.
string methodOperations provided by the Python string type for inspecting, transforming, and manipulating textual values.
textTextual data consists of character-based values representing words, phrases, or other written content rather than numeric quantities.
don't repeat yourself principleDon't repeat yourself is a software design principle that avoids duplicating logic or equivalent code.
normalizationNormalization is the organization of data into separate related structures so that each piece of information is stored only once.
headerA header is a file row containing names or other metadata that describe tabular columns.
datetime indexA pandas index whose labels are datetime values and which supports time-based selection and slicing.
is_quarter_endA pandas datetime property that returns Boolean values indicating whether datetime labels fall on the last day of a calendar quarter.
pandas StringDtypeA pandas extension dtype for string-valued data that supports pandas missing-value semantics without using the generic object dtype.
displotdisplot is a Seaborn function for visualizing the distribution of observations.
regplotregplot is a Seaborn function for visualizing data together with a fitted regression model.
header parameterThe header parameter specifies which input line contains column names or indicates that the input has no header.
index levelAn index level is one component of a hierarchical pandas index, containing labels for one dimension of row identification.
np.int64np.int64 is NumPy's signed 64-bit integer scalar and data type.
size attributeThe pandas size attribute reports the total number of values in a Series or DataFrame, including missing values.
axis parameterAn axis parameter specifies which pandas dimension, such as rows or columns, an operation should use.
dotted notationDot notation is attribute-access syntax that can retrieve a pandas column when its label is a valid Python identifier and does not conflict with an attribute.
pandas Series.idxminidxmin is a pandas operation that returns the index label associated with the minimum value.
dtypes methodThe dtypes method reports the data type of each column or value collection in a pandas object.
pandas to_numeric functionto_numeric is a pandas function that converts values to numeric data, optionally coercing unconvertible values to missing values.
left outer joinA left join retains rows driven by the left input and attaches matching rows from the right input when available.
JSONJSON is a text-based data-interchange format representing structured values such as arrays, objects, strings, numbers, and Boolean values.
read_jsonread_json is a pandas function that parses JSON data into a DataFrame or related pandas object.
standardValue standardization is the transformation of inconsistent categorical labels into a consistent set of labels.
z-scoreA z-score is the number of standard deviations by which a value differs from the mean of its data set.

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