diff --git a/online-event-resources/emerging-tech/Bots/Bots101/package-lock.json b/online-event-resources/emerging-tech/Bots/Bots101/package-lock.json index 1868f07..3c90793 100644 --- a/online-event-resources/emerging-tech/Bots/Bots101/package-lock.json +++ b/online-event-resources/emerging-tech/Bots/Bots101/package-lock.json @@ -760,9 +760,9 @@ } }, "find-my-way": { - "version": "2.2.3", - "resolved": "https://registry.npmjs.org/find-my-way/-/find-my-way-2.2.3.tgz", - "integrity": "sha512-C7dxfbX8pV1maLd31ygkBEOaD51Ls4dROuHjeSQZf1FeQinUzq3UA/kSPecLSDy9iAQufd8w1zgp7j64kyLdhw==", + "version": "2.2.5", + "resolved": "https://registry.npmjs.org/find-my-way/-/find-my-way-2.2.5.tgz", + "integrity": "sha512-GjRZZlGcGmTh9t+6Xrj5K0YprpoAFCAiCPgmAH9Kb09O4oX6hYuckDfnDipYj+Q7B1GtYWSzDI5HEecNYscLQg==", "requires": { "fast-decode-uri-component": "^1.0.0", "safe-regex2": "^2.0.0", diff --git a/workshop-resources/data-science-and-machine-learning/Data_Science_1/loan-project/loan-notebook.ipynb b/workshop-resources/data-science-and-machine-learning/Data_Science_1/loan-project/loan-notebook.ipynb index 9a688d9..2e09c88 100644 --- a/workshop-resources/data-science-and-machine-learning/Data_Science_1/loan-project/loan-notebook.ipynb +++ b/workshop-resources/data-science-and-machine-learning/Data_Science_1/loan-project/loan-notebook.ipynb @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -165,14 +165,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "text": "\u001b[1;31mSignature:\u001b[0m\n\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIO\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mAnyStr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'infer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0musecols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmangle_dupe_cols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mconverters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mtrue_values\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mfalse_values\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mskipinitialspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mskipfooter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mna_values\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mkeep_default_na\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mna_filter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m 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\u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'infer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mthousands\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mlineterminator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mquotechar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\"'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mquoting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdoublequote\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mescapechar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mcomment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdialect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0merror_bad_lines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mwarn_bad_lines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdelim_whitespace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mlow_memory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mfloat_precision\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nRead a comma-separated values (csv) file into DataFrame.\n\nAlso supports optionally iterating or breaking of the file\ninto chunks.\n\nAdditional help can be found in the online docs for\n`IO Tools `_.\n\nParameters\n----------\nfilepath_or_buffer : str, path object or file-like object\n Any valid string path is acceptable. The string could be a URL. Valid\n URL schemes include http, ftp, s3, and file. For file URLs, a host is\n expected. A local file could be: file://localhost/path/to/table.csv.\n\n If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n\n By file-like object, we refer to objects with a ``read()`` method, such as\n a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\nsep : str, default ','\n Delimiter to use. If sep is None, the C engine cannot automatically detect\n the separator, but the Python parsing engine can, meaning the latter will\n be used and automatically detect the separator by Python's builtin sniffer\n tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n different from ``'\\s+'`` will be interpreted as regular expressions and\n will also force the use of the Python parsing engine. Note that regex\n delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\ndelimiter : str, default ``None``\n Alias for sep.\nheader : int, list of int, default 'infer'\n Row number(s) to use as the column names, and the start of the\n data. Default behavior is to infer the column names: if no names\n are passed the behavior is identical to ``header=0`` and column\n names are inferred from the first line of the file, if column\n names are passed explicitly then the behavior is identical to\n ``header=None``. Explicitly pass ``header=0`` to be able to\n replace existing names. The header can be a list of integers that\n specify row locations for a multi-index on the columns\n e.g. [0,1,3]. Intervening rows that are not specified will be\n skipped (e.g. 2 in this example is skipped). Note that this\n parameter ignores commented lines and empty lines if\n ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n data rather than the first line of the file.\nnames : array-like, optional\n List of column names to use. If the file contains a header row,\n then you should explicitly pass ``header=0`` to override the column names.\n Duplicates in this list are not allowed.\nindex_col : int, str, sequence of int / str, or False, default ``None``\n Column(s) to use as the row labels of the ``DataFrame``, either given as\n string name or column index. If a sequence of int / str is given, a\n MultiIndex is used.\n\n Note: ``index_col=False`` can be used to force pandas to *not* use the first\n column as the index, e.g. when you have a malformed file with delimiters at\n the end of each line.\nusecols : list-like or callable, optional\n Return a subset of the columns. If list-like, all elements must either\n be positional (i.e. integer indices into the document columns) or strings\n that correspond to column names provided either by the user in `names` or\n inferred from the document header row(s). For example, a valid list-like\n `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n To instantiate a DataFrame from ``data`` with element order preserved use\n ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n in ``['foo', 'bar']`` order or\n ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n for ``['bar', 'foo']`` order.\n\n If callable, the callable function will be evaluated against the column\n names, returning names where the callable function evaluates to True. An\n example of a valid callable argument would be ``lambda x: x.upper() in\n ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n parsing time and lower memory usage.\nsqueeze : bool, default False\n If the parsed data only contains one column then return a Series.\nprefix : str, optional\n Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\nmangle_dupe_cols : bool, default True\n Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n 'X'...'X'. Passing in False will cause data to be overwritten if there\n are duplicate names in the columns.\ndtype : Type name or dict of column -> type, optional\n Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n 'c': 'Int64'}\n Use `str` or `object` together with suitable `na_values` settings\n to preserve and not interpret dtype.\n If converters are specified, they will be applied INSTEAD\n of dtype conversion.\nengine : {'c', 'python'}, optional\n Parser engine to use. The C engine is faster while the python engine is\n currently more feature-complete.\nconverters : dict, optional\n Dict of functions for converting values in certain columns. Keys can either\n be integers or column labels.\ntrue_values : list, optional\n Values to consider as True.\nfalse_values : list, optional\n Values to consider as False.\nskipinitialspace : bool, default False\n Skip spaces after delimiter.\nskiprows : list-like, int or callable, optional\n Line numbers to skip (0-indexed) or number of lines to skip (int)\n at the start of the file.\n\n If callable, the callable function will be evaluated against the row\n indices, returning True if the row should be skipped and False otherwise.\n An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\nskipfooter : int, default 0\n Number of lines at bottom of file to skip (Unsupported with engine='c').\nnrows : int, optional\n Number of rows of file to read. Useful for reading pieces of large files.\nna_values : scalar, str, list-like, or dict, optional\n Additional strings to recognize as NA/NaN. If dict passed, specific\n per-column NA values. By default the following values are interpreted as\n NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n '1.#IND', '1.#QNAN', '', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n 'nan', 'null'.\nkeep_default_na : bool, default True\n Whether or not to include the default NaN values when parsing the data.\n Depending on whether `na_values` is passed in, the behavior is as follows:\n\n * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n is appended to the default NaN values used for parsing.\n * If `keep_default_na` is True, and `na_values` are not specified, only\n the default NaN values are used for parsing.\n * If `keep_default_na` is False, and `na_values` are specified, only\n the NaN values specified `na_values` are used for parsing.\n * If `keep_default_na` is False, and `na_values` are not specified, no\n strings will be parsed as NaN.\n\n Note that if `na_filter` is passed in as False, the `keep_default_na` and\n `na_values` parameters will be ignored.\nna_filter : bool, default True\n Detect missing value markers (empty strings and the value of na_values). In\n data without any NAs, passing na_filter=False can improve the performance\n of reading a large file.\nverbose : bool, default False\n Indicate number of NA values placed in non-numeric columns.\nskip_blank_lines : bool, default True\n If True, skip over blank lines rather than interpreting as NaN values.\nparse_dates : bool or list of int or names or list of lists or dict, default False\n The behavior is as follows:\n\n * boolean. If True -> try parsing the index.\n * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n each as a separate date column.\n * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as\n a single date column.\n * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n result 'foo'\n\n If a column or index cannot be represented as an array of datetimes,\n say because of an unparseable value or a mixture of timezones, the column\n or index will be returned unaltered as an object data type. For\n non-standard datetime parsing, use ``pd.to_datetime`` after\n ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n specify ``date_parser`` to be a partially-applied\n :func:`pandas.to_datetime` with ``utc=True``. See\n :ref:`io.csv.mixed_timezones` for more.\n\n Note: A fast-path exists for iso8601-formatted dates.\ninfer_datetime_format : bool, default False\n If True and `parse_dates` is enabled, pandas will attempt to infer the\n format of the datetime strings in the columns, and if it can be inferred,\n switch to a faster method of parsing them. In some cases this can increase\n the parsing speed by 5-10x.\nkeep_date_col : bool, default False\n If True and `parse_dates` specifies combining multiple columns then\n keep the original columns.\ndate_parser : function, optional\n Function to use for converting a sequence of string columns to an array of\n datetime instances. The default uses ``dateutil.parser.parser`` to do the\n conversion. Pandas will try to call `date_parser` in three different ways,\n advancing to the next if an exception occurs: 1) Pass one or more arrays\n (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n string values from the columns defined by `parse_dates` into a single array\n and pass that; and 3) call `date_parser` once for each row using one or\n more strings (corresponding to the columns defined by `parse_dates`) as\n arguments.\ndayfirst : bool, default False\n DD/MM format dates, international and European format.\ncache_dates : bool, default True\n If True, use a cache of unique, converted dates to apply the datetime\n conversion. May produce significant speed-up when parsing duplicate\n date strings, especially ones with timezone offsets.\n\n .. versionadded:: 0.25.0\niterator : bool, default False\n Return TextFileReader object for iteration or getting chunks with\n ``get_chunk()``.\nchunksize : int, optional\n Return TextFileReader object for iteration.\n See the `IO Tools docs\n `_\n for more information on ``iterator`` and ``chunksize``.\ncompression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n For on-the-fly decompression of on-disk data. If 'infer' and\n `filepath_or_buffer` is path-like, then detect compression from the\n following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n decompression). If using 'zip', the ZIP file must contain only one data\n file to be read in. Set to None for no decompression.\nthousands : str, optional\n Thousands separator.\ndecimal : str, default '.'\n Character to recognize as decimal point (e.g. use ',' for European data).\nlineterminator : str (length 1), optional\n Character to break file into lines. Only valid with C parser.\nquotechar : str (length 1), optional\n The character used to denote the start and end of a quoted item. Quoted\n items can include the delimiter and it will be ignored.\nquoting : int or csv.QUOTE_* instance, default 0\n Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\ndoublequote : bool, default ``True``\n When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n whether or not to interpret two consecutive quotechar elements INSIDE a\n field as a single ``quotechar`` element.\nescapechar : str (length 1), optional\n One-character string used to escape other characters.\ncomment : str, optional\n Indicates remainder of line should not be parsed. If found at the beginning\n of a line, the line will be ignored altogether. This parameter must be a\n single character. Like empty lines (as long as ``skip_blank_lines=True``),\n fully commented lines are ignored by the parameter `header` but not by\n `skiprows`. For example, if ``comment='#'``, parsing\n ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n treated as the header.\nencoding : str, optional\n Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n standard encodings\n `_ .\ndialect : str or csv.Dialect, optional\n If provided, this parameter will override values (default or not) for the\n following parameters: `delimiter`, `doublequote`, `escapechar`,\n `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n override values, a ParserWarning will be issued. See csv.Dialect\n documentation for more details.\nerror_bad_lines : bool, default True\n Lines with too many fields (e.g. a csv line with too many commas) will by\n default cause an exception to be raised, and no DataFrame will be returned.\n If False, then these \"bad lines\" will dropped from the DataFrame that is\n returned.\nwarn_bad_lines : bool, default True\n If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n \"bad line\" will be output.\ndelim_whitespace : bool, default False\n Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be\n used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n is set to True, nothing should be passed in for the ``delimiter``\n parameter.\nlow_memory : bool, default True\n Internally process the file in chunks, resulting in lower memory use\n while parsing, but possibly mixed type inference. To ensure no mixed\n types either set False, or specify the type with the `dtype` parameter.\n Note that the entire file is read into a single DataFrame regardless,\n use the `chunksize` or `iterator` parameter to return the data in chunks.\n (Only valid with C parser).\nmemory_map : bool, default False\n If a filepath is provided for `filepath_or_buffer`, map the file object\n directly onto memory and access the data directly from there. Using this\n option can improve performance because there is no longer any I/O overhead.\nfloat_precision : str, optional\n Specifies which converter the C engine should use for floating-point\n values. The options are `None` for the ordinary converter,\n `high` for the high-precision converter, and `round_trip` for the\n round-trip converter.\n\nReturns\n-------\nDataFrame or TextParser\n A comma-separated values (csv) file is returned as two-dimensional\n data structure with labeled axes.\n\nSee Also\n--------\nto_csv : Write DataFrame to a comma-separated values (csv) file.\nread_csv : Read a comma-separated values (csv) file into DataFrame.\nread_fwf : Read a table of fixed-width formatted lines into DataFrame.\n\nExamples\n--------\n>>> pd.read_csv('data.csv') # doctest: +SKIP\n\u001b[1;31mFile:\u001b[0m c:\\users\\sarah\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\n\u001b[1;31mType:\u001b[0m function\n" - } - ], + "outputs": [], "source": [ "pd.read_csv?" ] @@ -193,36 +188,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": " ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\ncount 614.000000 614.000000 592.000000 600.00000 \nmean 5403.459283 1621.245798 146.412162 342.00000 \nstd 6109.041673 2926.248369 85.587325 65.12041 \nmin 150.000000 0.000000 9.000000 12.00000 \n25% 2877.500000 0.000000 100.000000 360.00000 \n50% 3812.500000 1188.500000 128.000000 360.00000 \n75% 5795.000000 2297.250000 168.000000 360.00000 \nmax 81000.000000 41667.000000 700.000000 480.00000 \n\n Credit_History \ncount 564.000000 \nmean 0.842199 \nstd 0.364878 \nmin 0.000000 \n25% 1.000000 \n50% 1.000000 \n75% 1.000000 \nmax 1.000000 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_History
count614.000000614.000000592.000000600.00000564.000000
mean5403.4592831621.245798146.412162342.000000.842199
std6109.0416732926.24836985.58732565.120410.364878
min150.0000000.0000009.00000012.000000.000000
25%2877.5000000.000000100.000000360.000001.000000
50%3812.5000001188.500000128.000000360.000001.000000
75%5795.0000002297.250000168.000000360.000001.000000
max81000.00000041667.000000700.000000480.000001.000000
\n
" - }, - "metadata": {}, - "execution_count": 3 - } - ], + "outputs": [], "source": [ "df.describe()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "\nRangeIndex: 614 entries, 0 to 613\nData columns (total 13 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Loan_ID 614 non-null object \n 1 Gender 601 non-null object \n 2 Married 611 non-null object \n 3 Dependents 599 non-null object \n 4 Education 614 non-null object \n 5 Self_Employed 582 non-null object \n 6 ApplicantIncome 614 non-null int64 \n 7 CoapplicantIncome 614 non-null float64\n 8 LoanAmount 592 non-null float64\n 9 Loan_Amount_Term 600 non-null float64\n 10 Credit_History 564 non-null float64\n 11 Property_Area 614 non-null object \n 12 Loan_Status 614 non-null object \ndtypes: float64(4), int64(1), object(8)\nmemory usage: 43.2+ KB\n" - } - ], + "outputs": [], "source": [ "df.info()" ] @@ -273,19 +252,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": " Loan_ID Dependents Education Self_Employed ApplicantIncome \\\n0 LP001002 0 Graduate No 5849 \n1 LP001003 1 Graduate No 4583 \n2 LP001005 0 Graduate Yes 3000 \n3 LP001006 0 Not Graduate No 2583 \n4 LP001008 0 Graduate No 6000 \n.. ... ... ... ... ... \n609 LP002978 0 Graduate No 2900 \n610 LP002979 3+ Graduate No 4106 \n611 LP002983 1 Graduate No 8072 \n612 LP002984 2 Graduate No 7583 \n613 LP002990 0 Graduate Yes 4583 \n\n CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History \\\n0 0.0 NaN 360.0 1.0 \n1 1508.0 128.0 360.0 1.0 \n2 0.0 66.0 360.0 1.0 \n3 2358.0 120.0 360.0 1.0 \n4 0.0 141.0 360.0 1.0 \n.. ... ... ... ... \n609 0.0 71.0 360.0 1.0 \n610 0.0 40.0 180.0 1.0 \n611 240.0 253.0 360.0 1.0 \n612 0.0 187.0 360.0 1.0 \n613 0.0 133.0 360.0 0.0 \n\n Property_Area Loan_Status \n0 Urban Y \n1 Rural N \n2 Urban Y \n3 Urban Y \n4 Urban Y \n.. ... ... \n609 Rural Y \n610 Rural Y \n611 Urban Y \n612 Urban Y \n613 Semiurban N \n\n[614 rows x 11 columns]", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Loan_IDDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status
0LP0010020GraduateNo58490.0NaN360.01.0UrbanY
1LP0010031GraduateNo45831508.0128.0360.01.0RuralN
2LP0010050GraduateYes30000.066.0360.01.0UrbanY
3LP0010060Not GraduateNo25832358.0120.0360.01.0UrbanY
4LP0010080GraduateNo60000.0141.0360.01.0UrbanY
....................................
609LP0029780GraduateNo29000.071.0360.01.0RuralY
610LP0029793+GraduateNo41060.040.0180.01.0RuralY
611LP0029831GraduateNo8072240.0253.0360.01.0UrbanY
612LP0029842GraduateNo75830.0187.0360.01.0UrbanY
613LP0029900GraduateYes45830.0133.0360.00.0SemiurbanN
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614 rows × 11 columns

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" - }, - "metadata": {}, - "execution_count": 5 - } - ], + "outputs": [], "source": [ "df.drop(columns=['Gender', 'Married'])" ] @@ -299,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -316,54 +285,27 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "No 500\nYes 82\nName: Self_Employed, dtype: int64" - }, - "metadata": {}, - "execution_count": 7 - } - ], + "outputs": [], "source": [ "df['Self_Employed'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Semiurban 233\nUrban 202\nRural 179\nName: Property_Area, dtype: int64" - }, - "metadata": {}, - "execution_count": 8 - } - ], + "outputs": [], "source": [ "df['Property_Area'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Graduate 480\nNot Graduate 134\nName: Education, dtype: int64" - }, - "metadata": {}, - "execution_count": 9 - } - ], + "outputs": [], "source": [ "df['Education'].value_counts()" ] @@ -384,29 +326,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 10 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "df.boxplot(column='ApplicantIncome')" ] @@ -456,29 +358,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 12 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "df.boxplot(column='ApplicantIncome', by = 'Education')" ] @@ -492,87 +374,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "df.boxplot(column='LoanAmount', by = 'Education')" ] @@ -588,29 +410,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 16 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "df.boxplot(column='LoanAmount', by = 'Self_Employed')" ] @@ -659,17 +461,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Frequency Table for Credit History:\n0.0 89\n1.0 475\nName: Credit_History, dtype: int64\n\nProbability of getting loan based on Credit_History:\n Loan_Status\nCredit_History \n0.0 0.078652\n1.0 0.795789\n" - } - ], + "outputs": [], "source": [ "# First we will create a frequency table on the Credit_History column to see how it is distributed.\n", "temp1 = df['Credit_History'].value_counts(ascending=True)\n", @@ -691,7 +487,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise: Find the Average of the Loan_Status variable" + "### Challenge: Find the Average of the Loan_Status variable" ] }, { @@ -703,16 +499,6 @@ "Hint: Use the `value_counts` function and then just divide the values." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Calling this note out again**\n", - "*Note from Sarah to Dan* \n", - "Is the empty cell below for them to fill in? \n", - "For each empty cell, we will need to add the \"answer\" in a separate cell. Could you make sure to add those in? This will be useful for instructors as they are prepping and for if/when we convert these to Learn modules." - ] - }, { "cell_type": "code", "execution_count": null, @@ -729,40 +515,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 18 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "fig = plt.figure(figsize=(8,4))\n", @@ -797,29 +552,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "temp3 = pd.crosstab(df['Credit_History'], df['Loan_Status'])\n", "temp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)" @@ -827,44 +562,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "text": "\u001b[1;31mSignature:\u001b[0m\n\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mrownames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mcolnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmargins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmargins_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'All'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdropna\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nCompute a simple cross tabulation of two (or more) factors. By default\ncomputes a frequency table of the factors unless an array of values and an\naggregation function are passed.\n\nParameters\n----------\nindex : array-like, Series, or list of arrays/Series\n Values to group by in the rows.\ncolumns : array-like, Series, or list of arrays/Series\n Values to group by in the columns.\nvalues : array-like, optional\n Array of values to aggregate according to the factors.\n Requires `aggfunc` be specified.\nrownames : sequence, default None\n If passed, must match number of row arrays passed.\ncolnames : sequence, default None\n If passed, must match number of column arrays passed.\naggfunc : function, optional\n If specified, requires `values` be specified as well.\nmargins : bool, default False\n Add row/column margins (subtotals).\nmargins_name : str, default 'All'\n Name of the row/column that will contain the totals\n when margins is True.\n\n .. versionadded:: 0.21.0\n\ndropna : bool, default True\n Do not include columns whose entries are all NaN.\nnormalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False\n Normalize by dividing all values by the sum of values.\n\n - If passed 'all' or `True`, will normalize over all values.\n - If passed 'index' will normalize over each row.\n - If passed 'columns' will normalize over each column.\n - If margins is `True`, will also normalize margin values.\n\nReturns\n-------\nDataFrame\n Cross tabulation of the data.\n\nSee Also\n--------\nDataFrame.pivot : Reshape data based on column values.\npivot_table : Create a pivot table as a DataFrame.\n\nNotes\n-----\nAny Series passed will have their name attributes used unless row or column\nnames for the cross-tabulation are specified.\n\nAny input passed containing Categorical data will have **all** of its\ncategories included in the cross-tabulation, even if the actual data does\nnot contain any instances of a particular category.\n\nIn the event that there aren't overlapping indexes an empty DataFrame will\nbe returned.\n\nExamples\n--------\n>>> a = np.array([\"foo\", \"foo\", \"foo\", \"foo\", \"bar\", \"bar\",\n... \"bar\", \"bar\", \"foo\", \"foo\", \"foo\"], dtype=object)\n>>> b = np.array([\"one\", \"one\", \"one\", \"two\", \"one\", \"one\",\n... \"one\", \"two\", \"two\", \"two\", \"one\"], dtype=object)\n>>> c = np.array([\"dull\", \"dull\", \"shiny\", \"dull\", \"dull\", \"shiny\",\n... \"shiny\", \"dull\", \"shiny\", \"shiny\", \"shiny\"],\n... dtype=object)\n>>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])\nb one two\nc dull shiny dull shiny\na\nbar 1 2 1 0\nfoo 2 2 1 2\n\nHere 'c' and 'f' are not represented in the data and will not be\nshown in the output because dropna is True by default. Set\ndropna=False to preserve categories with no data.\n\n>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])\n>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])\n>>> pd.crosstab(foo, bar)\ncol_0 d e\nrow_0\na 1 0\nb 0 1\n>>> pd.crosstab(foo, bar, dropna=False)\ncol_0 d e f\nrow_0\na 1 0 0\nb 0 1 0\nc 0 0 0\n\u001b[1;31mFile:\u001b[0m c:\\users\\sarah\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\pandas\\core\\reshape\\pivot.py\n\u001b[1;31mType:\u001b[0m function\n" - } - ], + "outputs": [], "source": [ "pd.crosstab?" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Loan_Status N Y\nCredit_History Gender \n0.0 Female 16 1\n Male 63 6\n1.0 Female 20 64\n Male 75 307", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Loan_StatusNY
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" - }, - "metadata": {}, - "execution_count": 21 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "temp3 = pd.crosstab([df['Credit_History'], df['Gender']], df['Loan_Status'],)\n", "temp3.plot(kind='bar', stacked=True, color=['red','blue'], )\n", @@ -900,14 +609,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "text": "\u001b[1;31mSignature:\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nApply a function along an axis of the DataFrame.\n\nObjects passed to the function are Series objects whose index is\neither the DataFrame's index (``axis=0``) or the DataFrame's columns\n(``axis=1``). By default (``result_type=None``), the final return type\nis inferred from the return type of the applied function. Otherwise,\nit depends on the `result_type` argument.\n\nParameters\n----------\nfunc : function\n Function to apply to each column or row.\naxis : {0 or 'index', 1 or 'columns'}, default 0\n Axis along which the function is applied:\n\n * 0 or 'index': apply function to each column.\n * 1 or 'columns': apply function to each row.\n\nraw : bool, default False\n Determines if row or column is passed as a Series or ndarray object:\n\n * ``False`` : passes each row or column as a Series to the\n function.\n * ``True`` : the passed function will receive ndarray objects\n instead.\n If you are just applying a NumPy reduction function this will\n achieve much better performance.\n\nresult_type : {'expand', 'reduce', 'broadcast', None}, default None\n These only act when ``axis=1`` (columns):\n\n * 'expand' : list-like results will be turned into columns.\n * 'reduce' : returns a Series if possible rather than expanding\n list-like results. This is the opposite of 'expand'.\n * 'broadcast' : results will be broadcast to the original shape\n of the DataFrame, the original index and columns will be\n retained.\n\n The default behaviour (None) depends on the return value of the\n applied function: list-like results will be returned as a Series\n of those. However if the apply function returns a Series these\n are expanded to columns.\n\n .. versionadded:: 0.23.0\n\nargs : tuple\n Positional arguments to pass to `func` in addition to the\n array/series.\n**kwds\n Additional keyword arguments to pass as keywords arguments to\n `func`.\n\nReturns\n-------\nSeries or DataFrame\n Result of applying ``func`` along the given axis of the\n DataFrame.\n\nSee Also\n--------\nDataFrame.applymap: For elementwise operations.\nDataFrame.aggregate: Only perform aggregating type operations.\nDataFrame.transform: Only perform transforming type operations.\n\nExamples\n--------\n\n>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])\n>>> df\n A B\n0 4 9\n1 4 9\n2 4 9\n\nUsing a numpy universal function (in this case the same as\n``np.sqrt(df)``):\n\n>>> df.apply(np.sqrt)\n A B\n0 2.0 3.0\n1 2.0 3.0\n2 2.0 3.0\n\nUsing a reducing function on either axis\n\n>>> df.apply(np.sum, axis=0)\nA 12\nB 27\ndtype: int64\n\n>>> df.apply(np.sum, axis=1)\n0 13\n1 13\n2 13\ndtype: int64\n\nReturning a list-like will result in a Series\n\n>>> df.apply(lambda x: [1, 2], axis=1)\n0 [1, 2]\n1 [1, 2]\n2 [1, 2]\ndtype: object\n\nPassing result_type='expand' will expand list-like results\nto columns of a Dataframe\n\n>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')\n 0 1\n0 1 2\n1 1 2\n2 1 2\n\nReturning a Series inside the function is similar to passing\n``result_type='expand'``. The resulting column names\nwill be the Series index.\n\n>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)\n foo bar\n0 1 2\n1 1 2\n2 1 2\n\nPassing ``result_type='broadcast'`` will ensure the same shape\nresult, whether list-like or scalar is returned by the function,\nand broadcast it along the axis. The resulting column names will\nbe the originals.\n\n>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')\n A B\n0 1 2\n1 1 2\n2 1 2\n\u001b[1;31mFile:\u001b[0m c:\\users\\sarah\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\pandas\\core\\frame.py\n\u001b[1;31mType:\u001b[0m method\n" - } - ], + "outputs": [], "source": [ "# The apply function applies a function across the axis of a dataframe.\n", "df.apply?" @@ -915,18 +619,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Loan_ID 0\nGender 13\nMarried 3\nDependents 15\nEducation 0\nSelf_Employed 32\nApplicantIncome 0\nCoapplicantIncome 0\nLoanAmount 22\nLoan_Amount_Term 14\nCredit_History 50\nProperty_Area 0\nLoan_Status 0\ndtype: int64" - }, - "metadata": {}, - "execution_count": 23 - } - ], + "outputs": [], "source": [ "df.apply(lambda x: sum(x.isnull()),axis=0)" ] @@ -956,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -965,18 +660,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Loan_ID 0\nGender 13\nMarried 3\nDependents 15\nEducation 0\nSelf_Employed 32\nApplicantIncome 0\nCoapplicantIncome 0\nLoanAmount 0\nLoan_Amount_Term 14\nCredit_History 50\nProperty_Area 0\nLoan_Status 0\ndtype: int64" - }, - "metadata": {}, - "execution_count": 25 - } - ], + "outputs": [], "source": [ "# Let's check and see, we shouldn't see any null values in the LoanAmount column\n", "df.apply(lambda x: sum(x.isnull()),axis=0)" @@ -984,18 +670,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "146.412162 22\n120.000000 20\n110.000000 17\n100.000000 15\n160.000000 12\n ..\n570.000000 1\n300.000000 1\n376.000000 1\n117.000000 1\n311.000000 1\nName: LoanAmount, Length: 204, dtype: int64" - }, - "metadata": {}, - "execution_count": 26 - } - ], + "outputs": [], "source": [ "# Now we can figure out what the fill value for loan amount is using value_counts\n", "df['LoanAmount'].value_counts()" @@ -1007,52 +684,16 @@ "source": [ "### Filling values in the Self_Employed Column\n", "\n", - "From our exploration step, we know that " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Note from Sarah to Dan* \n", - "Did you forget to finish the sentence above?" + "From our exploration step, we know that there are quite a few null values in the self employed column. Let's figure out a good way to fill the null values based on what we know about the data we do have." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "AxesSubplot(0.1,0.15;0.8x0.75)\nAxesSubplot(0.1,0.15;0.8x0.75)\n" - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "print(df.boxplot(column='LoanAmount', by = 'Education'))\n", "print(df.boxplot(column='LoanAmount', by = 'Self_Employed'))" @@ -1067,35 +708,20 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "No 500\nYes 82\nName: Self_Employed, dtype: int64" - }, - "metadata": {}, - "execution_count": 28 - } - ], + "outputs": [], "source": [ "df['Self_Employed'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Percentage of \"No\" values in the data is: 85.91065292096219 %\n" - } - ], + "outputs": [], "source": [ "# Now we can calculate the percentage of 'No' responses\n", "print('Percentage of \"No\" values in the data is:', 500/582*100,'%')" @@ -1110,17 +736,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "\nRangeIndex: 614 entries, 0 to 613\nData columns (total 13 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Loan_ID 614 non-null object \n 1 Gender 601 non-null object \n 2 Married 611 non-null object \n 3 Dependents 599 non-null object \n 4 Education 614 non-null object \n 5 Self_Employed 614 non-null object \n 6 ApplicantIncome 614 non-null int64 \n 7 CoapplicantIncome 614 non-null float64\n 8 LoanAmount 614 non-null float64\n 9 Loan_Amount_Term 600 non-null float64\n 10 Credit_History 564 non-null float64\n 11 Property_Area 614 non-null object \n 12 Loan_Status 614 non-null object \ndtypes: float64(4), int64(1), object(8)\nmemory usage: 43.2+ KB\n" - } - ], + "outputs": [], "source": [ "df['Self_Employed'].fillna('No',inplace=True)\n", "df.info()" @@ -1135,19 +755,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Education Graduate Not Graduate\nSelf_Employed \nNo 131.0 115.0\nYes 152.0 130.0", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
EducationGraduateNot Graduate
Self_Employed
No131.0115.0
Yes152.0130.0
\n
" - }, - "metadata": {}, - "execution_count": 31 - } - ], + "outputs": [], "source": [ "table = df.pivot_table(values='LoanAmount', index='Self_Employed', columns='Education', aggfunc=np.median)\n", "table" @@ -1155,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1166,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1176,17 +786,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "\nRangeIndex: 614 entries, 0 to 613\nData columns (total 13 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Loan_ID 614 non-null object \n 1 Gender 601 non-null object \n 2 Married 611 non-null object \n 3 Dependents 599 non-null object \n 4 Education 614 non-null object \n 5 Self_Employed 614 non-null object \n 6 ApplicantIncome 614 non-null int64 \n 7 CoapplicantIncome 614 non-null float64\n 8 LoanAmount 614 non-null float64\n 9 Loan_Amount_Term 600 non-null float64\n 10 Credit_History 564 non-null float64\n 11 Property_Area 614 non-null object \n 12 Loan_Status 614 non-null object \ndtypes: float64(4), int64(1), object(8)\nmemory usage: 43.2+ KB\n" - } - ], + "outputs": [], "source": [ "df.info()" ] @@ -1202,29 +806,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 35 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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- }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "# Let's pull the histgram again\n", "df['LoanAmount_log'] = np.log(df['LoanAmount'])\n", @@ -1238,14 +822,6 @@ "Now this distribution looks better. The effect that the higher limit values has been considerably reduced." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Note from Sarah to Dan* \n", - "Can you add more explanation for why the distribution looks \"better\". Even though it might be obvious, always good to qualify words like \"better\"" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1255,29 +831,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "" - }, - "metadata": {}, - "execution_count": 36 - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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kEU3jbRMMd62KtxCQNgbn3CWpQZ65b0At/mVf0nitWbgnuR54P3AJcHdV3blWx9LqOLUitW9Nwj3JJcAfAW8FzgD/mORIVT22Fseb1B8zpvGPKJIEazfnfg3wRFV9oaq+ARwGdq/RsSRJ50hVjX+nyc8B11fVL3bL7wF+pKp+eck2+4B93eLrgM8DVwL/NvaCNj77Mph9OZ89GazVvnxXVb1m0Iq1mnPPgLH/91Okqg4CB//fi5LjVTW3RjVtWPZlMPtyPnsy2MXYl7WaljkDXLVkeTvw9BodS5J0jrUK938EdiS5Osm3A3uAI2t0LEnSOdZkWqaqFpL8MvA3LF4K+cGqenQFLz24/CYXJfsymH05nz0Z7KLry5r8QVWSNFnefkCSGmS4S1KDJh7uSa5K8ndJTiV5NMmtk65pGiR5RZJPJ/mnri+/M+mapkmSS5J8JslfTrqWaZHkdJKTST6b5Pik65kWSS5Lcn+Sz3U582OTrmk9TMONwxaA/VX1cJLvAE4kObpWtyrYQF4EfrKq5pO8DPiHJH9dVQ9NurApcStwCnjVpAuZMruqqsUv64zi/cDHq+rnuqv3XjnpgtbDxM/cq+qZqnq4e/6fLH5gt022qsmrRfPd4su6f/71G0iyHbgBuHvStWi6JXkV8BbgHoCq+kZVPTfZqtbHxMN9qSSzwA8Dn5psJdOhm3r4LHAWOFpV9mXRHwK/AfzPpAuZMgV8IsmJ7vYegu8GvgL8STeNd3eSSydd1HqYmnBPsgX4CPCrVfUfk65nGlTVN6vqh1j8hu81Sd446ZomLcnbgbNVdWLStUyha6vqTcDPALckecukC5oCm4A3AX9cVT8MPA8cmGxJ62Mqwr2bU/4IcG9VfXTS9Uyb7tfIPnD9hEuZBtcC70hymsW7jf5kkj+dbEnToaqe7h7PAh9j8e6sF7szwJklv/Xez2LYN2/i4Z4kLM6Hnaqq3590PdMiyWuSXNY93wz8FPC5yVY1eVV1W1Vtr6pZFm9r8bdV9e4JlzVxSS7tLkigm3b4aeCRyVY1eVX1ZeBLSV7XDV0HXBQXa0zD1TLXAu8BTnbzywC/VVV/NcGapsFW4FD3H598G3BfVXnZny5kBvjY4rkSm4A/q6qPT7akqfE+4N7uSpkvAD8/4XrWhbcfkKQGTXxaRpI0foa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/AjM7GSOQHsSCAAAAAElFTkSuQmCC\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']\n", "df['TotalIncome_log'] = np.log(df['TotalIncome'])\n", @@ -1291,21 +847,13 @@ "The distribution again is better than before. You can decide whether or not you will continue the munging exercise with Gender, Married, Dependents, or the other variables. " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Note from Sarah to Dan* \n", - "Same comment as above, how is it \"better\"?" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building a predictive model in Python\n", "\n", - "So far we've spent a lot of time prepping our data getting it ready for our model. We'll be using a new library (for us) to code our model. Skicit-Learn (sklearn) is the most commonly used data science library in Python for this purpose.\n", + "So far we've spent a lot of time prepping our data getting it ready for our model. We'll be using a new library (for us) to code our model. Scikit-learn (sklearn) is the most commonly used data science library in Python for this purpose.\n", "\n", "Skicit-Learn requires that all inputs be numeric, but first let's quickly fill in all the null values within our data. \n", "\n", @@ -1314,11 +862,11 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Quick Fill\n", + "# Quick Fill of all the null values in the data\n", "df['Gender'].fillna(df['Gender'].mode()[0], inplace=True)\n", "df['Married'].fillna(df['Married'].mode()[0], inplace=True)\n", "df['Dependents'].fillna(df['Dependents'].mode()[0], inplace=True)\n", @@ -1326,28 +874,11 @@ "df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Note from Sarah to Dan* \n", - "Can you add comments to the next cell?" - ] - }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": "Loan_ID object\nGender int32\nMarried int32\nDependents int32\nEducation int32\nSelf_Employed int32\nApplicantIncome int64\nCoapplicantIncome float64\nLoanAmount float64\nLoan_Amount_Term float64\nCredit_History float64\nProperty_Area int32\nLoan_Status int32\nLoanAmount_log float64\nTotalIncome float64\nTotalIncome_log float64\ndtype: object" - }, - "metadata": {}, - "execution_count": 38 - } - ], + "outputs": [], "source": [ "# Here were are using LabelEncoder to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.\n", "from sklearn.preprocessing import LabelEncoder\n", @@ -1362,17 +893,11 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "\nRangeIndex: 614 entries, 0 to 613\nData columns (total 16 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Loan_ID 614 non-null object \n 1 Gender 614 non-null int32 \n 2 Married 614 non-null int32 \n 3 Dependents 614 non-null int32 \n 4 Education 614 non-null int32 \n 5 Self_Employed 614 non-null int32 \n 6 ApplicantIncome 614 non-null int64 \n 7 CoapplicantIncome 614 non-null float64\n 8 LoanAmount 614 non-null float64\n 9 Loan_Amount_Term 614 non-null float64\n 10 Credit_History 614 non-null float64\n 11 Property_Area 614 non-null int32 \n 12 Loan_Status 614 non-null int32 \n 13 LoanAmount_log 614 non-null float64\n 14 TotalIncome 614 non-null float64\n 15 TotalIncome_log 614 non-null float64\ndtypes: float64(7), int32(7), int64(1), object(1)\nmemory usage: 57.6+ KB\n" - } - ], + "outputs": [], "source": [ "# Let's check! All taken care of.\n", "df.info()" @@ -1380,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": { "scrolled": true }, @@ -1428,17 +953,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Accuracy : 80.945%\n" - } - ], + "outputs": [], "source": [ "# Let's first work with Credit_History - We start by assigning Loan_Status as the outcome variable\n", "outcome_var = 'Loan_Status'\n", @@ -1464,17 +983,11 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Accuracy : 80.945%\n" - } - ], + "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n", "\n", @@ -1499,17 +1012,11 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Accuracy : 96.743%\n" - } - ], + "outputs": [], "source": [ "# Let's try using the RandomForestClassifier model. Random Forest Classifier\n", "# A random forest is a meta estimator that fits a number of decision tree classifiers \n", @@ -1532,13 +1039,6 @@ "2. Avoid using complex modeling techniques as a black box without understanding the underlying concepts. Doing so would increase the tendency of overfitting thus making your models less interpretable\n", "3. Feature Engineering is the key to success. Everyone can use the prebuilt models but the real art and creativity lies in enhancing your features to better suit the model." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1557,7 +1057,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2-final" + "version": "3.7.4-final" } }, "nbformat": 4, diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/README.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/README.md new file mode 100644 index 0000000..65d9403 --- /dev/null +++ b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/README.md @@ -0,0 +1,23 @@ +# Creating decentralized apps (dapps) + +This workshop focuses on how you can build dapps, front-end applications for your smart contracts. It includes the following sections: + +1. Learn about the [fundamentals of blockchain products and development](fundamentals-of-products-development.md). +1. Understand what [dapps are and how they are used to build blockchain products](building-blockchain-products.md). +1. Read about the [technologies used for building dapps](tech-for-decentralized-apps.md). +1. Use [Drizzle to create your first dapp](create-your-first-dapp.md). +1. And use what you've learned to [create a dapp for a shipping contract](create-a-dapp-for-shipping.md) +1. Challenge: Create a crypto game item using tokens and [wire up the contract to a dapp](create-a-crypto-game-item.md). + +## Pre-requisites + +This workshop assumes you have knowledge of Blockchain fundamentals as well as the Ethereum platform. You should also have experience with writing smart contracts using Solidity. + +This workshop also assumes you have the following installed: + +- Git +- Node.js +- VS Code +- Blockchain Development Kit extension +- Truffle +- Ganache-CLI \ No newline at end of file diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/building-blockchain-products.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/building-blockchain-products.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-a-crypto-game-item.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-a-crypto-game-item.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-a-dapp-for-shipping.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-a-dapp-for-shipping.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-your-first-dapp.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/create-your-first-dapp.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/fundamentals-of-products-development.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/fundamentals-of-products-development.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/tech-for-decentralized-apps.md b/workshop-resources/emerging-tech/Blockchain_2/creating_decentralized_apps/tech-for-decentralized-apps.md new file mode 100644 index 0000000..e69de29 diff --git a/workshop-resources/emerging-tech/Blockchain_2/foundations/types-of-blockchains.md b/workshop-resources/emerging-tech/Blockchain_2/foundations/types-of-blockchains.md new file mode 100644 index 0000000..e69de29