Lab15(1)

Laboratory 15: Matplotlib for Jam! Juans-MacBook-Pro.local juanandreszuluqga /Users/juanandreszuluqga/anaconda3/bin/python 3.11.4 (main, Jul 5 2023, 09:00:44) [Clang 14.0.6 ] sys.version_info(major=3, minor=11, micro=4, releaselevel='final', serial=0) Full name: Juan Zuluaga R#: 11830028 Title of the notebook: Lab 15 Date: 11/13/2023 Matplotlip and Visual Display of Data This lesson will introduce the matplotlib external module package, and examine how to construct line charts, scatter plots, bar charts, and histograms using methods in matplotlib and pandas The theory of histograms will appear in later lessons, here we only show how to construct one using matplotlib About `matplotlib` Quoting from: https://matplotlib.org/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py matplotlib.pyplot is a collection of functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that "axes" here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis). Background Data are not always numerical. Data can music (audio files), or places on a map (georeferenced attributes files), images (various imge files, e.g. .png, jpeg) They can also be categorical into which you can place individuals: The individuals are cartons of ice-cream, and the category is the flavor in the carton The individuals are professional basketball players, and the category is the player's team. Bar Graphs Bar charts (graphs) are good display tools to graphically represent categorical information. The bars are evenly spaced and of constant width. The height/length of each bar is proportional to the relative frequency of the corresponding category. Relative frequency is the ratio of how many things in the category to how many things in the whole collection. The example below uses matplotlib to create a box plot for the ice cream analogy, the example is adapted from an example at https://www.geeksforgeeks.org/bar-plot-in-matplotlib/ Lets tidy up the script so it is more understandable, a small change in the import statement makes a simpler to read (for humans) script - also changed the bar colors just 'cause! Using pandas, we can build bar charts a bit easier. Flavor Number of Cartons 0 Chocolate 16 1 Strawberry 5 2 Vanilla 9 <Axes: xlabel='Flavor'> <Axes: xlabel='Flavor'> Example- Language Bars! Consider the data set "data" defined as data = {'C':20, 'C++':15, 'Java':30, 'Python':35} which lists student count by programming language in some school. Produce a bar chart of number of students in each language, where language is the classification, and student count is the variable. Plot it as a horizontal bar chart: Line Charts A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields. It is similar to a scatter plot (below) except that the measurement points are ordered (typically by their x-axis value) and joined with straight line segments. A line chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically. The x-axis spacing is sometimes tricky, hence line charts can unintentionally decieve - so be careful that it is the appropriate chart for your application. Example- Speed vs Time Consider the experimental data below Elapsed Time (s) Speed (m/s) 0 0 1.0 3 2.0 7 3.0 12 4.0 20 5.0 30 6.0 45.6 Show the relationship between time and speed. Is the relationship indicating acceleration? How much? From examination of the plot, estimate the speed at time t = 5.0 (eyeball estimate) Example- Add a linear fit Using the same series from Exercise 1, Plot the speed vs time (speed on y-axis, time on x-axis) using a line plot. Plot a second line based on the linear model , where . Example- Find a better fit Using trial and error try to improve the 'fit' of the model, by adjusting values of . Scatter Plots A scatter plot (also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are coded (color/shape/size), one additional variable can be displayed. The data are displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. A scatter plot can be used either when one continuous variable that is under the control of the experimenter and the other depends on it or when both continuous variables are independent. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables. A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on y axis and height would be on the x axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it indicates a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it indicates a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the relationship between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct solution in a finite time. No universal best-fit procedure is guaranteed to generate a solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree and to show nonlinear relationships between variables. Furthermore, if the data are represented by a mixture model of simple relationships, these relationships will be visually evident as superimposed patterns. Scatter charts can be built in the form of bubble, marker, or/and line charts. Much of the above is verbatim/adapted from: https://en.wikipedia.org/wiki/Scatter_plot Example- Examine the dataset with heights of fathers, mothers and sons --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[13], line 1 ----> 1 df = pd . read_csv( 'galton_subset.csv' ) 2 df[ 'child' ] = df[ 'son' ] ; df . drop( 'son' , axis =1 , inplace = True ) # rename son to child - got to imagine there are some daughters 3 df . head() File ~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:211 , in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper (*args, **kwargs) 209 else : 210 kwargs[new_arg_name] = new_arg_value --> 211 return func( * args, ** kwargs) File ~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:331 , in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper (*args, **kwargs) 325 if len (args) > num_allow_args: 326 warnings . warn( 327 msg . format(arguments = _format_argument_list(allow_args)), 328 FutureWarning , 329 stacklevel = find_stack_level(), 330 ) --> 331 return func( * args, ** kwargs) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950 , in read_csv (filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squee ze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filte r, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decima l, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whites pace, low_memory, memory_map, float_precision, storage_options) 935 kwds_defaults = _refine_defaults_read( 936 dialect, 937 delimiter, (...) 946 defaults = { "delimiter" : "," }, 947 ) 948 kwds . update(kwds_defaults) --> 950 return _read(filepath_or_buffer, kwds) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605 , in _read (filepath_or_buffer, kwds) 602 _validate_names(kwds . get( "names" , None )) 604 # Create the parser. --> 605 parser = TextFileReader(filepath_or_buffer, ** kwds) 607 if chunksize or iterator: 608 return parser File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442 , in TextFileReader.__init__ (self, f, engine, **kwds) 1439 self . options[ "has_index_names" ] = kwds[ "has_index_names" ] 1441 self . handles: IOHandles | None = None -> 1442 self . _engine = self . _make_engine(f, self . engine) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735 , in TextFileReader._make_engine (self, f, engine) 1733 if "b" not in mode: 1734 mode += "b" -> 1735 self . handles = get_handle( 1736 f, 1737 mode, 1738 encoding = self . options . get( "encoding" , None ), 1739 compression = self . options . get( "compression" , None ), 1740 memory_map = self . options . get( "memory_map" , False ), 1741 is_text = is_text, 1742 errors = self . options . get( "encoding_errors" , "strict" ), 1743 storage_options = self . options . get( "storage_options" , None ), 1744 ) 1745 assert self . handles is not None 1746 f = self . handles . handle File ~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:856 , in get_handle (path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_opti ons) 851 elif isinstance (handle, str ): 852 # Check whether the filename is to be opened in binary mode. 853 # Binary mode does not support 'encoding' and 'newline'. 854 if ioargs . encoding and "b" not in ioargs . mode: 855 # Encoding --> 856 handle = open ( 857 handle, 858 ioargs . mode, 859 encoding = ioargs . encoding, 860 errors = errors, 861 newline = "" , 862 ) 863 else : 864 # Binary mode 865 handle = open (handle, ioargs . mode) FileNotFoundError : [Errno 2] No such file or directory: 'galton_subset.csv' --------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[14], line 2 1 myfamily = plt . figure(figsize = ( 10 , 10 )) # build a square drawing canvass from figure class ----> 2 plt . scatter(son, dad, c = 'red' ) # basic scatter plot 3 plt . show() NameError : name 'son' is not defined <Figure size 1000x1000 with 0 Axes> --------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[15], line 3 1 # Looks lousy, needs some labels 2 myfamily = plt . figure(figsize = ( 10 , 10 )) # build a square drawing canvass from figure class ----> 3 plt . scatter(son, dad, c = 'red' , label = 'Father' ) # one plot series 4 plt . scatter(son, mom, c = 'blue' , label = 'Mother' ) # two plot series 5 plt . xlabel( "Child's height" ) NameError : name 'son' is not defined <Figure size 1000x1000 with 0 Axes> --------------------------------------------------------------------------- KeyError Traceback (most recent call last) File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802 , in Index.get_loc (self, key, method, tolerance) 3801 try : -> 3802 return self . _engine . get_loc(casted_key) 3803 except KeyError as err: File ~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:138 , in pandas._libs.index.IndexEngine.get_loc () File ~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:165 , in pandas._libs.index.IndexEngine.get_loc () File pandas/_libs/hashtable_class_helper.pxi:5745 , in pandas._libs.hashtable.PyObjectHashTable.get_item () File pandas/_libs/hashtable_class_helper.pxi:5753 , in pandas._libs.hashtable.PyObjectHashTable.get_item () KeyError : 'child' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[16], line 2 1 # Repeat in pandas - The dataframe already is built ----> 2 df . plot . scatter(x = "child" , y = "father" ) File ~/anaconda3/lib/python3.11/site-packages/pandas/plotting/_core.py:1697 , in PlotAccessor.scatter (self, x, y, s, c, **kwargs) 1614 def scatter ( self , x, y, s = None , c = None , ** kwargs) -> PlotAccessor: 1615 """ 1616 Create a scatter plot with varying marker point size and color. 1617 (...) 1695 ... colormap='viridis') 1696 """ -> 1697 return self (kind = "scatter" , x = x, y = y, s = s, c = c, ** kwargs) File ~/anaconda3/lib/python3.11/site-packages/pandas/plotting/_core.py:945 , in PlotAccessor.__call__ (self, *args, **kwargs) 943 if kind in self . _dataframe_kinds: 944 if isinstance (data, ABCDataFrame): --> 945 return plot_backend . plot(data, x = x, y = y, kind = kind, ** kwargs) 946 else : 947 raise ValueError ( f"plot kind { kind } can only be used for data frames" ) File ~/anaconda3/lib/python3.11/site-packages/pandas/plotting/_matplotlib/__init__.py:71 , in plot (data, kind, **kwargs) 69 kwargs[ "ax" ] = getattr (ax, "left_ax" , ax) 70 plot_obj = PLOT_CLASSES[kind](data, ** kwargs) ---> 71 plot_obj . generate() 72 plot_obj . draw() 73 return plot_obj . result File ~/anaconda3/lib/python3.11/site-packages/pandas/plotting/_matplotlib/core.py:452 , in MPLPlot.generate (self) 450 self . _compute_plot_data() 451 self . _setup_subplots() --> 452 self . _make_plot() 453 self . _add_table() 454 self . _make_legend() File ~/anaconda3/lib/python3.11/site-packages/pandas/plotting/_matplotlib/core.py:1260 , in ScatterPlot._make_plot (self) 1257 else : 1258 label = None 1259 scatter = ax . scatter( -> 1260 data[x] . values, 1261 data[y] . values, 1262 c = c_values, 1263 label = label, 1264 cmap = cmap, 1265 norm = norm, 1266 ** self . kwds, 1267 ) 1268 if cb: 1269 cbar_label = c if c_is_column else "" File ~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3807 , in DataFrame.__getitem__ (self, key) 3805 if self . columns . nlevels > 1 : 3806 return self . _getitem_multilevel(key) -> 3807 indexer = self . columns . get_loc(key) 3808 if is_integer(indexer): 3809 indexer = [indexer] File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3804 , in Index.get_loc (self, key, method, tolerance) 3802 return self . _engine . get_loc(casted_key) 3803 except KeyError as err: -> 3804 raise KeyError (key) from err 3805 except TypeError : 3806 # If we have a listlike key, _check_indexing_error will raise 3807 # InvalidIndexError. Otherwise we fall through and re-raise 3808 # the TypeError. 3809 self . _check_indexing_error(key) KeyError : 'child' Histograms Quoting from https://en.wikipedia.org/wiki/Histogram "A histogram is an approximate representation of the distribution of numerical data. It was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" (or "bucket") the range of values— that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and are often (but not required to be) of equal size. If the bins are of equal size, a rectangle is erected over the bin with height proportional to the frequency—the number of cases in each bin. A histogram may also be normalized to display "relative" frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. However, bins need not be of equal width; in that case, the erected rectangle is defined to have its area proportional to the frequency of cases in the bin. The vertical axis is then not the frequency but frequency density—the number of cases per unit of the variable on the horizontal axis. Examples of variable bin width are displayed on Census bureau data below. As the adjacent bins leave no gaps, the rectangles of a histogram touch each other to indicate that the original variable is continuous. Histograms give a rough sense of the density of the underlying distribution of the data, and often for density estimation: estimating the probability density function of the underlying variable. The total area of a histogram used for probability density is always normalized to 1. If the length of the intervals on the x-axis are all 1, then a histogram is identical to a relative frequency plot. A histogram can be thought of as a simplistic kernel density estimation, which uses a kernel to smooth frequencies over the bins. This yields a smoother probability density function, which will in general more accurately reflect distribution of the underlying variable. The density estimate could be plotted as an alternative to the histogram, and is usually drawn as a curve rather than a set of boxes. Histograms are nevertheless preferred in applications, when their statistical properties need to be modeled. The correlated variation of a kernel density estimate is very difficult to describe mathematically, while it is simple for a histogram where each bin varies independently. An alternative to kernel density estimation is the average shifted histogram, which is fast to compute and gives a smooth curve estimate of the density without using kernels. The histogram is one of the seven basic tools of quality control. Histograms are sometimes confused with bar charts. A histogram is used for continuous data, where the bins represent ranges of data, while a bar chart is a plot of categorical variables. Some authors recommend that bar charts have gaps between the rectangles to clarify the distinction." Example- Explore the "top_movies" dataset and draw histograms for Gross and Year. --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[17], line 3 1 import pandas as pd ----> 3 df = pd . read_csv( 'top_movies.csv' ) 4 df . head() File ~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:211 , in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper (*args, **kwargs) 209 else : 210 kwargs[new_arg_name] = new_arg_value --> 211 return func( * args, ** kwargs) File ~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:331 , in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper (*args, **kwargs) 325 if len (args) > num_allow_args: 326 warnings . warn( 327 msg . format(arguments = _format_argument_list(allow_args)), 328 FutureWarning , 329 stacklevel = find_stack_level(), 330 ) --> 331 return func( * args, ** kwargs) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950 , in read_csv (filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squee ze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filte r, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decima l, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whites pace, low_memory, memory_map, float_precision, storage_options) 935 kwds_defaults = _refine_defaults_read( 936 dialect, 937 delimiter, (...) 946 defaults = { "delimiter" : "," }, 947 ) 948 kwds . update(kwds_defaults) --> 950 return _read(filepath_or_buffer, kwds) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605 , in _read (filepath_or_buffer, kwds) 602 _validate_names(kwds . get( "names" , None )) 604 # Create the parser. --> 605 parser = TextFileReader(filepath_or_buffer, ** kwds) 607 if chunksize or iterator: 608 return parser File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442 , in TextFileReader.__init__ (self, f, engine, **kwds) 1439 self . options[ "has_index_names" ] = kwds[ "has_index_names" ] 1441 self . handles: IOHandles | None = None -> 1442 self . _engine = self . _make_engine(f, self . engine) File ~/anaconda3/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735 , in TextFileReader._make_engine (self, f, engine) 1733 if "b" not in mode: 1734 mode += "b" -> 1735 self . handles = get_handle( 1736 f, 1737 mode, 1738 encoding = self . options . get( "encoding" , None ), 1739 compression = self . options . get( "compression" , None ), 1740 memory_map = self . options . get( "memory_map" , False ), 1741 is_text = is_text, 1742 errors = self . options . get( "encoding_errors" , "strict" ), 1743 storage_options = self . options . get( "storage_options" , None ), 1744 ) 1745 assert self . handles is not None 1746 f = self . handles . handle File ~/anaconda3/lib/python3.11/site-packages/pandas/io/common.py:856 , in get_handle (path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_opti ons) 851 elif isinstance (handle, str ): 852 # Check whether the filename is to be opened in binary mode. 853 # Binary mode does not support 'encoding' and 'newline'. 854 if ioargs . encoding and "b" not in ioargs . mode: 855 # Encoding --> 856 handle = open ( 857 handle, 858 ioargs . mode, 859 encoding = ioargs . encoding, 860 errors = errors, 861 newline = "" , 862 ) 863 else : 864 # Binary mode 865 handle = open (handle, ioargs . mode) FileNotFoundError : [Errno 2] No such file or directory: 'top_movies.csv' --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[18], line 1 ----> 1 df[[ "Gross" ]] . hist() File ~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3813 , in DataFrame.__getitem__ (self, key) 3811 if is_iterator(key): 3812 key = list (key) -> 3813 indexer = self . columns . _get_indexer_strict(key, "columns" )[ 1 ] 3815 # take() does not accept boolean indexers 3816 if getattr (indexer, "dtype" , None ) == bool : File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6070 , in Index._get_indexer_strict (self, key, axis_name) 6067 else : 6068 keyarr, indexer, new_indexer = self . _reindex_non_unique(keyarr) -> 6070 self . _raise_if_missing(keyarr, indexer, axis_name) 6072 keyarr = self . take(indexer) 6073 if isinstance (key, Index): 6074 # GH 42790 - Preserve name from an Index File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6130 , in Index._raise_if_missing (self, key, indexer, axis_name) 6128 if use_interval_msg: 6129 key = list (key) -> 6130 raise KeyError ( f"None of [ { key } ] are in the [ { axis_name } ]" ) 6132 not_found = list (ensure_index(key)[missing_mask . nonzero()[ 0 ]] . unique()) 6133 raise KeyError ( f" { not_found } not in index" ) KeyError : "None of [Index(['Gross'], dtype='object')] are in the [columns]" --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[19], line 1 ----> 1 df[[ "Year" ]] . hist() File ~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3813 , in DataFrame.__getitem__ (self, key) 3811 if is_iterator(key): 3812 key = list (key) -> 3813 indexer = self . columns . _get_indexer_strict(key, "columns" )[ 1 ] 3815 # take() does not accept boolean indexers 3816 if getattr (indexer, "dtype" , None ) == bool : File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6070 , in Index._get_indexer_strict (self, key, axis_name) 6067 else : 6068 keyarr, indexer, new_indexer = self . _reindex_non_unique(keyarr) -> 6070 self . _raise_if_missing(keyarr, indexer, axis_name) 6072 keyarr = self . take(indexer) 6073 if isinstance (key, Index): 6074 # GH 42790 - Preserve name from an Index File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6130 , in Index._raise_if_missing (self, key, indexer, axis_name) 6128 if use_interval_msg: 6129 key = list (key) -> 6130 raise KeyError ( f"None of [ { key } ] are in the [ { axis_name } ]" ) 6132 not_found = list (ensure_index(key)[missing_mask . nonzero()[ 0 ]] . unique()) 6133 raise KeyError ( f" { not_found } not in index" ) KeyError : "None of [Index(['Year'], dtype='object')] are in the [columns]" --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[20], line 1 ----> 1 df[[ "Gross" ]] . hist(bins =100 ) File ~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3813 , in DataFrame.__getitem__ (self, key) 3811 if is_iterator(key): 3812 key = list (key) -> 3813 indexer = self . columns . _get_indexer_strict(key, "columns" )[ 1 ] 3815 # take() does not accept boolean indexers 3816 if getattr (indexer, "dtype" , None ) == bool : File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6070 , in Index._get_indexer_strict (self, key, axis_name) 6067 else : 6068 keyarr, indexer, new_indexer = self . _reindex_non_unique(keyarr) -> 6070 self . _raise_if_missing(keyarr, indexer, axis_name) 6072 keyarr = self . take(indexer) 6073 if isinstance (key, Index): 6074 # GH 42790 - Preserve name from an Index File ~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:6130 , in Index._raise_if_missing (self, key, indexer, axis_name) 6128 if use_interval_msg: 6129 key = list (key) -> 6130 raise KeyError ( f"None of [ { key } ] are in the [ { axis_name } ]" ) 6132 not_found = list (ensure_index(key)[missing_mask . nonzero()[ 0 ]] . unique()) 6133 raise KeyError ( f" { not_found } not in index" ) KeyError : "None of [Index(['Gross'], dtype='object')] are in the [columns]" This is a Matplotlib Cheat Sheet Here are some of the resources used for creating this notebook: "Discrete distribution as horizontal bar chart" available at * https://matplotlib.org/stable/gallery/lines_bars_and_markers/horizontal_barchart_distribution.html "Bar Plot in Matplotlib" available at * https://www.geeksforgeeks.org/bar-plot-in-matplotlib/ Here are some great reads on this topic: "Python | Introduction to Matplotlib" available at * https://www.geeksforgeeks.org/python-introduction-matplotlib/ "Visualization with Matplotlib" available at * https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html "Introduction to Matplotlib — Data Visualization in Python" by Ehi Aigiomawu available at * https://heartbeat.fritz.ai/introduction-to-matplotlib-data-visualization-in-python-d9143287ae39 "Python Plotting With Matplotlib (Guide)" by Brad Solomon available at * https://realpython.com/python-matplotlib-guide/ Here are some great videos on these topics: "Matplotlib Tutorial (Part 1): Creating and Customizing Our First Plots" by Corey Schafer available at * https://www.youtube.com/watch?v=UO98lJQ3QGI "Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial" by CS Dojo available at * https://www.youtube.com/watch?v=a9UrKTVEeZA "Intro to Data Visualization in Python with Matplotlib! (line graph, bar chart, title, labels, size)" by Keith Galli available at * https://www.youtube.com/watch?v=DAQNHzOcO5A Exercise: Bins, Bins, Bins! Selecting the number of bins is an important decision when working with histograms. Are there any rules or recommendations for choosing the number or width of bins? What happens if we use too many or too few bins? * Make sure to cite any resources that you may use. Explain here... Too Few Bins: A histogram with insufficient detail may conceal underlying patterns in the data if there are too few bins used. It could simplify the distribution too much and cause crucial information to be lost. Too Many Bins: Using too many bins can result in a "noisy" histogram that makes it difficult to see the distribution's overall shape. Overinterpretation may result, making it more difficult to spot important trends or patterns. In conclusion, choosing the right number of bins for a histogram requires striking a balance between preserving key details in your data and offering an understandable, instructive visualization. When making this choice, you should take into account the type of data you have, how it is distributed, and the objectives of your analysis. In [1]: # Preamble script block to identify host, user, and kernel import sys ! hostname ! whoami print ( sys . executable ) print ( sys . version ) print ( sys . version_info ) In [2]: ice_cream = { 'Chocolate' : 16 , 'Strawberry' : 5 , 'Vanilla' : 9 } # build a data model import matplotlib.pyplot # the python plotting library flavors = list ( ice_cream . keys ()) # make a list object based on flavors cartons = list ( ice_cream . values ()) # make a list object based on carton count -- assumes 1:1 association! myfigure = matplotlib . pyplot . figure ( figsize = ( 10 , 5 )) # generate a object from the figure class, set aspect ratio # Built the plot matplotlib . pyplot . bar ( flavors , cartons , color = 'maroon' , width = 0.8 ) matplotlib . pyplot . xlabel ( "Flavors" ) matplotlib . pyplot . ylabel ( "No. of Cartons in Stock" ) matplotlib . pyplot . title ( "Current Ice Cream in Storage" ) matplotlib . pyplot . show () In [3]: ice_cream = { 'Chocolate' : 16 , 'Strawberry' : 5 , 'Vanilla' : 9 } # build a data model import matplotlib.pyplot as plt # the python plotting library flavors = list ( ice_cream . keys ()) # make a list object based on flavors cartons = list ( ice_cream . values ()) # make a list object based on carton count -- assumes 1:1 association! myfigure = plt . figure ( figsize = ( 10 , 5 )) # generate a object from the figure class, set aspect ratio # Built the plot plt . bar ( flavors , cartons , color = 'orange' , width = 0.8 ) plt . xlabel ( "Flavors" ) plt . ylabel ( "No. of Cartons in Stock" ) plt . title ( "Current Ice Cream in Storage" ) plt . show () In [4]: import pandas as pd my_data = { "Flavor" : [ 'Chocolate' , 'Strawberry' , 'Vanilla' ], "Number of Cartons" : [ 16 , 5 , 9 ] } df = pd . DataFrame ( my_data ) df . head () Out[4]: In [5]: df . plot . bar ( x = 'Flavor' , y = 'Number of Cartons' , color = 'magenta' ) Out[5]: In [6]: df . plot . bar ( x = 'Flavor' , y = 'Number of Cartons' , color = "red" ) # rotate the category labels Out[6]: In [7]: # Code and run your solution here import numpy as np import matplotlib.pyplot as plt # creating the dataset data = { 'C' : 20 , 'C++' : 15 , 'Java' : 30 , 'Python' : 35 } courses = list ( data . keys ()) values = list ( data . values ()) fig = plt . figure ( figsize = ( 10 , 5 )) # creating the bar plot plt . bar ( courses , values , color = 'maroon' , width = 0.4 ) plt . xlabel ( "Courses offered" ) plt . ylabel ( "No. of students enrolled" ) plt . title ( "Students enrolled in different courses" ) plt . show () In [8]: # Code and run your solution here # creating the dataset data = { 'C' : 20 , 'C++' : 15 , 'Java' : 30 , 'Python' : 35 } courses = list ( data . keys ()) values = list ( data . values ()) fig = plt . figure ( figsize = ( 10 , 5 )) # creating the bar plot plt . barh ( courses , values , color = 'maroon' , height = 0.4 ) plt . xlabel ( "Courses offered" ) plt . ylabel ( "No. of students enrolled" ) plt . title ( "Students enrolled in different courses" ) plt . show () In [9]: # Create two lists; time and speed. time = [ 0 , 1.0 , 2.0 , 3.0 , 4.0 , 5.0 , 6.0 ] speed = [ 0 , 3 , 7 , 12 , 20 , 30 , 45.6 ] In [10]: # Create a line chart of speed on y axis and time on x axis mydata = plt . figure ( figsize = ( 10 , 5 )) # build a square drawing canvass from figure class plt . plot ( time , speed , c = 'red' , marker = 'v' , linewidth = 1 ) # basic line plot plt . title ( "Speed over time" ) plt . show () In [11]: # Code and run your solution here: def ymodel ( xmodel , slope , intercept ): ymodel = slope * xmodel + intercept return ( ymodel ) yseries = [] slope = 7.6 intercept = 0.0 for i in range ( 0 , len ( time )): yseries . append ( ymodel ( time [ i ], slope , intercept )) # Create a markers only line chart mydata = plt . figure ( figsize = ( 10 , 5 )) # build a square drawing canvass from figure class plt . plot ( time , speed , c = 'red' , marker = '^' , linewidth = 0.5 ) # basic line plot plt . plot ( time , yseries , c = 'blue' ) plt . show () In [12]: # Code and run your solution here: yseries = [] slope = 7.6 intercept = - 8.0 for i in range ( 0 , len ( time )): yseries . append ( ymodel ( time [ i ], slope , intercept )) # Create a markers only line chart mydata = plt . figure ( figsize = ( 10 , 5 )) # build a square drawing canvass from figure class plt . plot ( time , speed , c = 'red' , marker = '^' , linewidth = 0 ) # basic scatter plot plt . plot ( time , yseries , c = 'blue' ) plt . show () In [13]: df = pd . read_csv ( 'galton_subset.csv' ) df [ 'child' ] = df [ 'son' ] ; df . drop ( 'son' , axis = 1 , inplace = True ) # rename son to child - got to imagine there are some daughters df . head () In [ ]: # build some lists dad = df [ 'father' ] ; mom = df [ 'mother' ] ; son = df [ 'child' ] In [14]: myfamily = plt . figure ( figsize = ( 10 , 10 )) # build a square drawing canvass from figure class plt . scatter ( son , dad , c = 'red' ) # basic scatter plot plt . show () In [15]: # Looks lousy, needs some labels myfamily = plt . figure ( figsize = ( 10 , 10 )) # build a square drawing canvass from figure class plt . scatter ( son , dad , c = 'red' , label = 'Father' ) # one plot series plt . scatter ( son , mom , c = 'blue' , label = 'Mother' ) # two plot series plt . xlabel ( "Child's height" ) plt . ylabel ( "Parents' height" ) plt . legend () plt . show () # render the two plots In [16]: # Repeat in pandas - The dataframe already is built df . plot . scatter ( x = "child" , y = "father" ) In [ ]: ax = df . plot . scatter ( x = "child" , y = "father" , c = "red" , label = 'Father' ) df . plot . scatter ( x = "child" , y = "mother" , c = "blue" , label = 'Mother' , ax = ax ) ax . set_xlabel ( "Child's height" ) ax . set_ylabel ( "Parents' Height" ) In [17]: import pandas as pd df = pd . read_csv ( 'top_movies.csv' ) df . head () In [18]: df [[ "Gross" ]] . hist () In [19]: df [[ "Year" ]] . hist () In [20]: df [[ "Gross" ]] . hist ( bins = 100 )