{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview of Pandas and Xarray groupby(), resample()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get some data : tornado reports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## read the csv file - check out the options" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodydatetimetimezonestatestateFIPSStateNumber...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
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5 rows × 29 columns

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" ], "text/plain": [ " om yr mo dy date time timezone state stateFIPS StateNumber \\\n", "0 1 1950 1 3 1/3/50 11:00:00 3 MO 29 1 \n", "1 1 1950 1 3 1/3/50 11:00:00 3 MO 29 1 \n", "2 1 1950 1 3 1/3/50 11:10:00 3 IL 17 1 \n", "3 2 1950 1 3 1/3/50 11:55:00 3 IL 17 2 \n", "4 3 1950 1 3 1/3/50 16:00:00 3 OH 39 1 \n", "\n", " ... lenghtmiles widthyards ns sn sg fips1 fips2 fips3 fips4 fc \n", "0 ... 9.5 150.0 2 0 1 0 0 0 0 0 \n", "1 ... 6.2 150.0 2 1 2 189 0 0 0 0 \n", "2 ... 3.3 100.0 2 1 2 119 0 0 0 0 \n", "3 ... 3.6 130.0 1 1 1 135 0 0 0 0 \n", "4 ... 0.1 10.0 1 1 1 161 0 0 0 0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = pd.read_csv('1950-2016_all_tornadoes.csv', delimiter=',', header=0)\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(d)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'infer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mas_recarray\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompact_ints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_unsigned\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat_precision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Read CSV (comma-separated) file into DataFrame\n", "\n", "Also supports optionally iterating or breaking of the file\n", "into chunks.\n", "\n", "Additional help can be found in the `online docs for IO Tools\n", "`_.\n", "\n", "Parameters\n", "----------\n", "filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)\n", " The string could be a URL. Valid URL schemes include http, ftp, s3, and\n", " file. For file URLs, a host is expected. For instance, a local file could\n", " be file ://localhost/path/to/table.csv\n", "sep : 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'``\n", "delimiter : str, default ``None``\n", " Alternative argument name for sep.\n", "delim_whitespace : boolean, 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.\n", "\n", " .. versionadded:: 0.18.1 support for the Python parser.\n", "\n", "header : int or list of ints, 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.\n", "names : array-like, default None\n", " List of column names to use. If file contains no header row, then you\n", " should explicitly pass header=None. Duplicates in this list will cause\n", " a ``UserWarning`` to be issued.\n", "index_col : int or sequence or False, default None\n", " Column to use as the row labels of the DataFrame. If a sequence is given, a\n", " MultiIndex is used. If you have a malformed file with delimiters at the end\n", " of each line, you might consider index_col=False to force pandas to _not_\n", " use the first column as the index (row names)\n", "usecols : array-like or callable, default None\n", " Return a subset of the columns. If array-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 array-like\n", " `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz'].\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.\n", "as_recarray : boolean, default False\n", " .. deprecated:: 0.19.0\n", " Please call `pd.read_csv(...).to_records()` instead.\n", "\n", " Return a NumPy recarray instead of a DataFrame after parsing the data.\n", " If set to True, this option takes precedence over the `squeeze` parameter.\n", " In addition, as row indices are not available in such a format, the\n", " `index_col` parameter will be ignored.\n", "squeeze : boolean, default False\n", " If the parsed data only contains one column then return a Series\n", "prefix : str, default None\n", " Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n", "mangle_dupe_cols : boolean, default True\n", " Duplicate columns will be specified as 'X.0'...'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.\n", "dtype : Type name or dict of column -> type, default None\n", " Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n", " Use `str` or `object` to preserve and not interpret dtype.\n", " If converters are specified, they will be applied INSTEAD\n", " of dtype conversion.\n", "engine : {'c', 'python'}, optional\n", " Parser engine to use. The C engine is faster while the python engine is\n", " currently more feature-complete.\n", "converters : dict, default None\n", " Dict of functions for converting values in certain columns. Keys can either\n", " be integers or column labels\n", "true_values : list, default None\n", " Values to consider as True\n", "false_values : list, default None\n", " Values to consider as False\n", "skipinitialspace : boolean, default False\n", " Skip spaces after delimiter.\n", "skiprows : list-like or integer or callable, default None\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]``.\n", "skipfooter : int, default 0\n", " Number of lines at bottom of file to skip (Unsupported with engine='c')\n", "skip_footer : int, default 0\n", " .. deprecated:: 0.19.0\n", " Use the `skipfooter` parameter instead, as they are identical\n", "nrows : int, default None\n", " Number of rows of file to read. Useful for reading pieces of large files\n", "na_values : scalar, str, list-like, or dict, default None\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', 'nan',\n", " 'null'.\n", "keep_default_na : bool, default True\n", " If na_values are specified and keep_default_na is False the default NaN\n", " values are overridden, otherwise they're appended to.\n", "na_filter : boolean, 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\n", "verbose : boolean, default False\n", " Indicate number of NA values placed in non-numeric columns\n", "skip_blank_lines : boolean, default True\n", " If True, skip over blank lines rather than interpreting as NaN values\n", "parse_dates : boolean or list of ints or names or list of lists or dict, default False\n", "\n", " * boolean. If True -> try parsing the index.\n", " * list of ints 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 result\n", " 'foo'\n", "\n", " If a column or index contains an unparseable date, the entire column or\n", " index will be returned unaltered as an object data type. For non-standard\n", " datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``\n", "\n", " Note: A fast-path exists for iso8601-formatted dates.\n", "infer_datetime_format : boolean, 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.\n", "keep_date_col : boolean, default False\n", " If True and `parse_dates` specifies combining multiple columns then\n", " keep the original columns.\n", "date_parser : function, default None\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.\n", "dayfirst : boolean, default False\n", " DD/MM format dates, international and European format\n", "iterator : boolean, default False\n", " Return TextFileReader object for iteration or getting chunks with\n", " ``get_chunk()``.\n", "chunksize : int, default None\n", " Return TextFileReader object for iteration.\n", " See the `IO Tools docs\n", " `_\n", " for more information on ``iterator`` and ``chunksize``.\n", "compression : {'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.\n", "\n", " .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.\n", "\n", "thousands : str, default None\n", " Thousands separator\n", "decimal : str, default '.'\n", " Character to recognize as decimal point (e.g. use ',' for European data).\n", "float_precision : string, default None\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", "lineterminator : str (length 1), default None\n", " Character to break file into lines. Only valid with C parser.\n", "quotechar : 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.\n", "quoting : 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).\n", "doublequote : boolean, 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.\n", "escapechar : str (length 1), default None\n", " One-character string used to escape delimiter when quoting is QUOTE_NONE.\n", "comment : str, default None\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 '#empty\\na,b,c\\n1,2,3'\n", " with `header=0` will result in 'a,b,c' being\n", " treated as the header.\n", "encoding : str, default None\n", " Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n", " standard encodings\n", " `_\n", "dialect : str or csv.Dialect instance, default None\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.\n", "tupleize_cols : boolean, default False\n", " .. deprecated:: 0.21.0\n", " This argument will be removed and will always convert to MultiIndex\n", "\n", " Leave a list of tuples on columns as is (default is to convert to\n", " a MultiIndex on the columns)\n", "error_bad_lines : boolean, 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.\n", "warn_bad_lines : boolean, 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.\n", "low_memory : boolean, 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)\n", "buffer_lines : int, default None\n", " .. deprecated:: 0.19.0\n", " This argument is not respected by the parser\n", "compact_ints : boolean, default False\n", " .. deprecated:: 0.19.0\n", " Argument moved to ``pd.to_numeric``\n", "\n", " If compact_ints is True, then for any column that is of integer dtype,\n", " the parser will attempt to cast it as the smallest integer dtype possible,\n", " either signed or unsigned depending on the specification from the\n", " `use_unsigned` parameter.\n", "use_unsigned : boolean, default False\n", " .. deprecated:: 0.19.0\n", " Argument moved to ``pd.to_numeric``\n", "\n", " If integer columns are being compacted (i.e. `compact_ints=True`), specify\n", " whether the column should be compacted to the smallest signed or unsigned\n", " integer dtype.\n", "memory_map : boolean, 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.\n", "\n", "Returns\n", "-------\n", "result : DataFrame or TextParser\n", "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/pangeo/lib/python3.6/site-packages/pandas/io/parsers.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_csv?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I can tell read_csv to parse some columns and create a time index" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yr_mo_dy_timeomyrmodydatetimetimezonestatestateFIPS...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
01950-01-03 11:00:0011950131/3/5011:00:003MO29...9.5150.020100000
11950-01-03 11:00:0011950131/3/5011:00:003MO29...6.2150.02121890000
21950-01-03 11:10:0011950131/3/5011:10:003IL17...3.3100.02121190000
31950-01-03 11:55:0021950131/3/5011:55:003IL17...3.6130.01111350000
41950-01-03 16:00:0031950131/3/5016:00:003OH39...0.110.01111610000
\n", "

5 rows × 30 columns

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" ], "text/plain": [ " yr_mo_dy_time om yr mo dy date time timezone state \\\n", "0 1950-01-03 11:00:00 1 1950 1 3 1/3/50 11:00:00 3 MO \n", "1 1950-01-03 11:00:00 1 1950 1 3 1/3/50 11:00:00 3 MO \n", "2 1950-01-03 11:10:00 1 1950 1 3 1/3/50 11:10:00 3 IL \n", "3 1950-01-03 11:55:00 2 1950 1 3 1/3/50 11:55:00 3 IL \n", "4 1950-01-03 16:00:00 3 1950 1 3 1/3/50 16:00:00 3 OH \n", "\n", " stateFIPS ... lenghtmiles widthyards ns sn sg fips1 fips2 fips3 \\\n", "0 29 ... 9.5 150.0 2 0 1 0 0 0 \n", "1 29 ... 6.2 150.0 2 1 2 189 0 0 \n", "2 17 ... 3.3 100.0 2 1 2 119 0 0 \n", "3 17 ... 3.6 130.0 1 1 1 135 0 0 \n", "4 39 ... 0.1 10.0 1 1 1 161 0 0 \n", "\n", " fips4 fc \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = pd.read_csv('1950-2016_all_tornadoes.csv', \n", " delimiter=',',\n", " header=0,\n", " error_bad_lines=False, \n", " parse_dates=[[1,2,3,5]], keep_date_col=True)\n", "d.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Useful commands to always run to check on how the data were loaded" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 62208 entries, 0 to 62207\n", "Data columns (total 30 columns):\n", "yr_mo_dy_time 62208 non-null datetime64[ns]\n", "om 62208 non-null int64\n", "yr 62208 non-null object\n", "mo 62208 non-null object\n", "dy 62208 non-null object\n", "date 62208 non-null object\n", "time 62208 non-null object\n", "timezone 62208 non-null int64\n", "state 62208 non-null object\n", "stateFIPS 62208 non-null int64\n", "StateNumber 62208 non-null int64\n", "EFscale 62208 non-null int64\n", "injuries 62208 non-null int64\n", "fatalities 62208 non-null int64\n", "loss 62208 non-null float64\n", "croploss 62208 non-null float64\n", "startlat 62208 non-null float64\n", "startlon 62208 non-null float64\n", "endlat 62208 non-null float64\n", "endlon 62208 non-null float64\n", "lenghtmiles 62208 non-null float64\n", "widthyards 62208 non-null float64\n", "ns 62208 non-null int64\n", "sn 62208 non-null int64\n", "sg 62208 non-null int64\n", "fips1 62208 non-null int64\n", "fips2 62208 non-null int64\n", "fips3 62208 non-null int64\n", "fips4 62208 non-null int64\n", "fc 62208 non-null int64\n", "dtypes: datetime64[ns](1), float64(8), int64(15), object(6)\n", "memory usage: 14.2+ MB\n" ] } ], "source": [ "d.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### some of the variables were loaded as objects although - except for the state name - they are all numbers.\n", "#### quick way to convert them is the following ( I will get an error because indeed the state names can't be converted)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home2/nhn2/miniconda3/envs/pangeo/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n", "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 62208 entries, 0 to 62207\n", "Data columns (total 30 columns):\n", "yr_mo_dy_time 62208 non-null datetime64[ns]\n", "om 62208 non-null int64\n", "yr 62208 non-null int64\n", "mo 62208 non-null int64\n", "dy 62208 non-null int64\n", "date 62208 non-null object\n", "time 62208 non-null object\n", "timezone 62208 non-null int64\n", "state 62208 non-null object\n", "stateFIPS 62208 non-null int64\n", "StateNumber 62208 non-null int64\n", "EFscale 62208 non-null int64\n", "injuries 62208 non-null int64\n", "fatalities 62208 non-null int64\n", "loss 62208 non-null float64\n", "croploss 62208 non-null float64\n", "startlat 62208 non-null float64\n", "startlon 62208 non-null float64\n", "endlat 62208 non-null float64\n", "endlon 62208 non-null float64\n", "lenghtmiles 62208 non-null float64\n", "widthyards 62208 non-null float64\n", "ns 62208 non-null int64\n", "sn 62208 non-null int64\n", "sg 62208 non-null int64\n", "fips1 62208 non-null int64\n", "fips2 62208 non-null int64\n", "fips3 62208 non-null int64\n", "fips4 62208 non-null int64\n", "fc 62208 non-null int64\n", "dtypes: datetime64[ns](1), float64(8), int64(18), object(3)\n", "memory usage: 14.2+ MB\n" ] } ], "source": [ "d = d.convert_objects(convert_numeric=True)\n", "d.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### .describe() gives an overview of the data\n", "#### look at EFscale" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodytimezonestateFIPSStateNumberEFscaleinjuriesfatalities...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
count62208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.000000...62208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.00000062208.000000
mean50792.7252441988.4453775.95833315.9202033.00106129.32597125.8901430.8100411.7270450.110420...3.626159103.0319281.0248520.9902260.996367105.2687278.5997941.8147020.6012570.029964
std148896.67835518.0306522.3993638.7536240.07606615.01390632.4806510.95846121.1325961.673987...8.843966204.3015750.1603550.0983780.46052797.07977937.87658117.1224939.8753270.170489
min1.0000001950.0000001.0000001.0000000.0000001.0000000.000000-9.0000000.0000000.000000...0.0000000.0000000.0000000.000000-9.0000000.0000000.0000000.0000000.0000000.000000
25%250.0000001974.0000004.0000008.0000003.00000018.0000004.0000000.0000000.0000000.000000...0.10000017.0000001.0000001.0000001.00000039.0000000.0000000.0000000.0000000.000000
50%517.0000001991.0000006.00000016.0000003.00000029.00000014.0000001.0000000.0000000.000000...0.70000040.0000001.0000001.0000001.00000085.0000000.0000000.0000000.0000000.000000
75%870.0000002004.0000007.00000024.0000003.00000045.00000035.0000001.0000000.0000000.000000...3.000000100.0000001.0000001.0000001.000000137.0000000.0000000.0000000.0000000.000000
max614471.0000002016.00000012.00000031.0000009.00000072.000000232.0000005.0000001740.000000158.000000...234.7000004576.0000003.0000001.0000002.000000810.000000810.000000710.000000507.0000001.000000
\n", "

8 rows × 26 columns

\n", "
" ], "text/plain": [ " om yr mo dy timezone \\\n", "count 62208.000000 62208.000000 62208.000000 62208.000000 62208.000000 \n", "mean 50792.725244 1988.445377 5.958333 15.920203 3.001061 \n", "std 148896.678355 18.030652 2.399363 8.753624 0.076066 \n", "min 1.000000 1950.000000 1.000000 1.000000 0.000000 \n", "25% 250.000000 1974.000000 4.000000 8.000000 3.000000 \n", "50% 517.000000 1991.000000 6.000000 16.000000 3.000000 \n", "75% 870.000000 2004.000000 7.000000 24.000000 3.000000 \n", "max 614471.000000 2016.000000 12.000000 31.000000 9.000000 \n", "\n", " stateFIPS StateNumber EFscale injuries fatalities \\\n", "count 62208.000000 62208.000000 62208.000000 62208.000000 62208.000000 \n", "mean 29.325971 25.890143 0.810041 1.727045 0.110420 \n", "std 15.013906 32.480651 0.958461 21.132596 1.673987 \n", "min 1.000000 0.000000 -9.000000 0.000000 0.000000 \n", "25% 18.000000 4.000000 0.000000 0.000000 0.000000 \n", "50% 29.000000 14.000000 1.000000 0.000000 0.000000 \n", "75% 45.000000 35.000000 1.000000 0.000000 0.000000 \n", "max 72.000000 232.000000 5.000000 1740.000000 158.000000 \n", "\n", " ... lenghtmiles widthyards ns sn \\\n", "count ... 62208.000000 62208.000000 62208.000000 62208.000000 \n", "mean ... 3.626159 103.031928 1.024852 0.990226 \n", "std ... 8.843966 204.301575 0.160355 0.098378 \n", "min ... 0.000000 0.000000 0.000000 0.000000 \n", "25% ... 0.100000 17.000000 1.000000 1.000000 \n", "50% ... 0.700000 40.000000 1.000000 1.000000 \n", "75% ... 3.000000 100.000000 1.000000 1.000000 \n", "max ... 234.700000 4576.000000 3.000000 1.000000 \n", "\n", " sg fips1 fips2 fips3 fips4 \\\n", "count 62208.000000 62208.000000 62208.000000 62208.000000 62208.000000 \n", "mean 0.996367 105.268727 8.599794 1.814702 0.601257 \n", "std 0.460527 97.079779 37.876581 17.122493 9.875327 \n", "min -9.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 1.000000 39.000000 0.000000 0.000000 0.000000 \n", "50% 1.000000 85.000000 0.000000 0.000000 0.000000 \n", "75% 1.000000 137.000000 0.000000 0.000000 0.000000 \n", "max 2.000000 810.000000 810.000000 710.000000 507.000000 \n", "\n", " fc \n", "count 62208.000000 \n", "mean 0.029964 \n", "std 0.170489 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 \n", "\n", "[8 rows x 26 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Accessing columns/rows can be simple or tricky depending on how complicated it is.\n", "#### In general, columns can be accessed \"attribute style\" or through the key names\n", "Attribute style" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 3\n", "1 3\n", "2 3\n", "3 3\n", "4 1\n", "Name: EFscale, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.EFscale.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "keys" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['yr_mo_dy_time', 'om', 'yr', 'mo', 'dy', 'date', 'time', 'timezone',\n", " 'state', 'stateFIPS', 'StateNumber', 'EFscale', 'injuries',\n", " 'fatalities', 'loss', 'croploss', 'startlat', 'startlon', 'endlat',\n", " 'endlon', 'lenghtmiles', 'widthyards', 'ns', 'sn', 'sg', 'fips1',\n", " 'fips2', 'fips3', 'fips4', 'fc'],\n", " dtype='object')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.keys()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 3\n", "1 3\n", "2 3\n", "3 3\n", "4 1\n", "Name: EFscale, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['EFscale'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### let's tell python to exclude those negative EFscale" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodytimezonestateFIPSStateNumberEFscaleinjuriesfatalities...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
count62178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.000000...62178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.00000062178.000000
mean50520.9790761988.4320825.95847415.9196023.00106129.32571025.9026340.8147741.7278780.110473...3.627471103.0542021.0248640.9902220.996365105.2789898.6039441.8155780.6015470.029978
std148417.61455518.0248372.3997098.7534900.07608415.01272532.4835060.93414921.1376591.674389...8.845853204.3458080.1603930.0984020.46063897.07770037.88524717.1265779.8777000.170529
min1.0000001950.0000001.0000001.0000000.0000001.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-9.0000000.0000000.0000000.0000000.0000000.000000
25%250.0000001974.0000004.0000008.0000003.00000018.0000004.0000000.0000000.0000000.000000...0.10000017.0000001.0000001.0000001.00000039.0000000.0000000.0000000.0000000.000000
50%517.0000001991.0000006.00000016.0000003.00000029.00000014.0000001.0000000.0000000.000000...0.70000040.0000001.0000001.0000001.00000085.0000000.0000000.0000000.0000000.000000
75%869.0000002004.0000007.00000024.0000003.00000045.00000035.0000001.0000000.0000000.000000...3.010000100.0000001.0000001.0000001.000000137.0000000.0000000.0000000.0000000.000000
max614471.0000002016.00000012.00000031.0000009.00000072.000000232.0000005.0000001740.000000158.000000...234.7000004576.0000003.0000001.0000002.000000810.000000810.000000710.000000507.0000001.000000
\n", "

8 rows × 26 columns

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" ], "text/plain": [ " om yr mo dy timezone \\\n", "count 62178.000000 62178.000000 62178.000000 62178.000000 62178.000000 \n", "mean 50520.979076 1988.432082 5.958474 15.919602 3.001061 \n", "std 148417.614555 18.024837 2.399709 8.753490 0.076084 \n", "min 1.000000 1950.000000 1.000000 1.000000 0.000000 \n", "25% 250.000000 1974.000000 4.000000 8.000000 3.000000 \n", "50% 517.000000 1991.000000 6.000000 16.000000 3.000000 \n", "75% 869.000000 2004.000000 7.000000 24.000000 3.000000 \n", "max 614471.000000 2016.000000 12.000000 31.000000 9.000000 \n", "\n", " stateFIPS StateNumber EFscale injuries fatalities \\\n", "count 62178.000000 62178.000000 62178.000000 62178.000000 62178.000000 \n", "mean 29.325710 25.902634 0.814774 1.727878 0.110473 \n", "std 15.012725 32.483506 0.934149 21.137659 1.674389 \n", "min 1.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 18.000000 4.000000 0.000000 0.000000 0.000000 \n", "50% 29.000000 14.000000 1.000000 0.000000 0.000000 \n", "75% 45.000000 35.000000 1.000000 0.000000 0.000000 \n", "max 72.000000 232.000000 5.000000 1740.000000 158.000000 \n", "\n", " ... lenghtmiles widthyards ns sn \\\n", "count ... 62178.000000 62178.000000 62178.000000 62178.000000 \n", "mean ... 3.627471 103.054202 1.024864 0.990222 \n", "std ... 8.845853 204.345808 0.160393 0.098402 \n", "min ... 0.000000 0.000000 0.000000 0.000000 \n", "25% ... 0.100000 17.000000 1.000000 1.000000 \n", "50% ... 0.700000 40.000000 1.000000 1.000000 \n", "75% ... 3.010000 100.000000 1.000000 1.000000 \n", "max ... 234.700000 4576.000000 3.000000 1.000000 \n", "\n", " sg fips1 fips2 fips3 fips4 \\\n", "count 62178.000000 62178.000000 62178.000000 62178.000000 62178.000000 \n", "mean 0.996365 105.278989 8.603944 1.815578 0.601547 \n", "std 0.460638 97.077700 37.885247 17.126577 9.877700 \n", "min -9.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 1.000000 39.000000 0.000000 0.000000 0.000000 \n", "50% 1.000000 85.000000 0.000000 0.000000 0.000000 \n", "75% 1.000000 137.000000 0.000000 0.000000 0.000000 \n", "max 2.000000 810.000000 810.000000 710.000000 507.000000 \n", "\n", " fc \n", "count 62178.000000 \n", "mean 0.029978 \n", "std 0.170529 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 \n", "\n", "[8 rows x 26 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d[d.EFscale>-1]\n", "\n", "d.describe()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 61062.000000\n", "mean 37.159091\n", "std 5.123198\n", "min 18.130000\n", "25% 33.245400\n", "50% 37.100000\n", "75% 40.970000\n", "max 61.020000\n", "Name: startlat, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d= d[d['sg']==1]\n", "d.startlat.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### let's rename the date column" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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date_timeomyrmodydatetimetimezonestatestateFIPS...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
01950-01-03 11:00:0011950131/3/5011:00:003MO29...9.5150.020100000
31950-01-03 11:55:0021950131/3/5011:55:003IL17...3.6130.01111350000
41950-01-03 16:00:0031950131/3/5016:00:003OH39...0.110.01111610000
51950-01-13 05:25:00419501131/13/505:25:003AR5...0.617.01111130000
61950-01-25 19:30:00519501251/25/5019:30:003MO29...2.3300.0111930000
\n", "

5 rows × 30 columns

\n", "
" ], "text/plain": [ " date_time om yr mo dy date time timezone state \\\n", "0 1950-01-03 11:00:00 1 1950 1 3 1/3/50 11:00:00 3 MO \n", "3 1950-01-03 11:55:00 2 1950 1 3 1/3/50 11:55:00 3 IL \n", "4 1950-01-03 16:00:00 3 1950 1 3 1/3/50 16:00:00 3 OH \n", "5 1950-01-13 05:25:00 4 1950 1 13 1/13/50 5:25:00 3 AR \n", "6 1950-01-25 19:30:00 5 1950 1 25 1/25/50 19:30:00 3 MO \n", "\n", " stateFIPS ... lenghtmiles widthyards ns sn sg fips1 fips2 fips3 \\\n", "0 29 ... 9.5 150.0 2 0 1 0 0 0 \n", "3 17 ... 3.6 130.0 1 1 1 135 0 0 \n", "4 39 ... 0.1 10.0 1 1 1 161 0 0 \n", "5 5 ... 0.6 17.0 1 1 1 113 0 0 \n", "6 29 ... 2.3 300.0 1 1 1 93 0 0 \n", "\n", " fips4 fc \n", "0 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 \n", "6 0 0 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d.rename(index=str, columns={\"yr_mo_dy_time\": \"date_time\"})\n", "d.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### let's use the date_time column as an index" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodydatetimetimezonestatestateFIPSStateNumber...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
date_time
1950-01-03 11:00:0011950131/3/5011:00:003MO291...9.5150.020100000
1950-01-03 11:55:0021950131/3/5011:55:003IL172...3.6130.01111350000
1950-01-03 16:00:0031950131/3/5016:00:003OH391...0.110.01111610000
1950-01-13 05:25:00419501131/13/505:25:003AR51...0.617.01111130000
1950-01-25 19:30:00519501251/25/5019:30:003MO292...2.3300.0111930000
\n", "

5 rows × 29 columns

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" ], "text/plain": [ " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-01-03 11:00:00 1 1950 1 3 1/3/50 11:00:00 3 MO \n", "1950-01-03 11:55:00 2 1950 1 3 1/3/50 11:55:00 3 IL \n", "1950-01-03 16:00:00 3 1950 1 3 1/3/50 16:00:00 3 OH \n", "1950-01-13 05:25:00 4 1950 1 13 1/13/50 5:25:00 3 AR \n", "1950-01-25 19:30:00 5 1950 1 25 1/25/50 19:30:00 3 MO \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-01-03 11:00:00 29 1 ... 9.5 150.0 2 \n", "1950-01-03 11:55:00 17 2 ... 3.6 130.0 1 \n", "1950-01-03 16:00:00 39 1 ... 0.1 10.0 1 \n", "1950-01-13 05:25:00 5 1 ... 0.6 17.0 1 \n", "1950-01-25 19:30:00 29 2 ... 2.3 300.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-01-03 11:00:00 0 1 0 0 0 0 0 \n", "1950-01-03 11:55:00 1 1 135 0 0 0 0 \n", "1950-01-03 16:00:00 1 1 161 0 0 0 0 \n", "1950-01-13 05:25:00 1 1 113 0 0 0 0 \n", "1950-01-25 19:30:00 1 1 93 0 0 0 0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.set_index(keys='date_time', inplace=True)\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodydatetimetimezonestatestateFIPSStateNumber...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
date_time
1960-01-12 14:20:00119601121/12/6014:20:003MO291...0.117.01111470000
1960-01-14 01:30:00219601141/14/601:30:003TX481...2.033.01114150000
1960-01-14 06:00:00319601141/14/606:00:003OK401...0.110.01111230000
1960-01-14 06:35:00419601141/14/606:35:003TX482...0.217.01111810000
1960-01-14 07:45:00519601141/14/607:45:003TX483...0.333.01112230000
1960-01-14 11:20:00619601141/14/6011:20:003LA221...4.520.0111170000
1960-01-14 12:15:00719601141/14/6012:15:003AR51...6.117.0111450000
1960-01-28 09:40:00819601281/28/609:40:003TX484...2.033.01112450000
1960-01-29 06:00:00919601291/29/606:00:003AL11...0.110.0111970000
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9 rows × 29 columns

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" ], "text/plain": [ " om yr mo dy date time timezone state \\\n", "date_time \n", "1960-01-12 14:20:00 1 1960 1 12 1/12/60 14:20:00 3 MO \n", "1960-01-14 01:30:00 2 1960 1 14 1/14/60 1:30:00 3 TX \n", "1960-01-14 06:00:00 3 1960 1 14 1/14/60 6:00:00 3 OK \n", "1960-01-14 06:35:00 4 1960 1 14 1/14/60 6:35:00 3 TX \n", "1960-01-14 07:45:00 5 1960 1 14 1/14/60 7:45:00 3 TX \n", "1960-01-14 11:20:00 6 1960 1 14 1/14/60 11:20:00 3 LA \n", "1960-01-14 12:15:00 7 1960 1 14 1/14/60 12:15:00 3 AR \n", "1960-01-28 09:40:00 8 1960 1 28 1/28/60 9:40:00 3 TX \n", "1960-01-29 06:00:00 9 1960 1 29 1/29/60 6:00:00 3 AL \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1960-01-12 14:20:00 29 1 ... 0.1 17.0 1 \n", "1960-01-14 01:30:00 48 1 ... 2.0 33.0 1 \n", "1960-01-14 06:00:00 40 1 ... 0.1 10.0 1 \n", "1960-01-14 06:35:00 48 2 ... 0.2 17.0 1 \n", "1960-01-14 07:45:00 48 3 ... 0.3 33.0 1 \n", "1960-01-14 11:20:00 22 1 ... 4.5 20.0 1 \n", "1960-01-14 12:15:00 5 1 ... 6.1 17.0 1 \n", "1960-01-28 09:40:00 48 4 ... 2.0 33.0 1 \n", "1960-01-29 06:00:00 1 1 ... 0.1 10.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1960-01-12 14:20:00 1 1 147 0 0 0 0 \n", "1960-01-14 01:30:00 1 1 415 0 0 0 0 \n", "1960-01-14 06:00:00 1 1 123 0 0 0 0 \n", "1960-01-14 06:35:00 1 1 181 0 0 0 0 \n", "1960-01-14 07:45:00 1 1 223 0 0 0 0 \n", "1960-01-14 11:20:00 1 1 17 0 0 0 0 \n", "1960-01-14 12:15:00 1 1 45 0 0 0 0 \n", "1960-01-28 09:40:00 1 1 245 0 0 0 0 \n", "1960-01-29 06:00:00 1 1 97 0 0 0 0 \n", "\n", "[9 rows x 29 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['1960-01-01':'1960-02-01']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## let's look at groupby" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Group series using mapper (dict or key function, apply given function\n", "to group, return result as series) or by a series of columns.\n", "\n", "Parameters\n", "----------\n", "by : mapping, function, str, or iterable\n", " Used to determine the groups for the groupby.\n", " If ``by`` is a function, it's called on each value of the object's\n", " index. If a dict or Series is passed, the Series or dict VALUES\n", " will be used to determine the groups (the Series' values are first\n", " aligned; see ``.align()`` method). If an ndarray is passed, the\n", " values are used as-is determine the groups. A str or list of strs\n", " may be passed to group by the columns in ``self``\n", "axis : int, default 0\n", "level : int, level name, or sequence of such, default None\n", " If the axis is a MultiIndex (hierarchical), group by a particular\n", " level or levels\n", "as_index : boolean, default True\n", " For aggregated output, return object with group labels as the\n", " index. Only relevant for DataFrame input. as_index=False is\n", " effectively \"SQL-style\" grouped output\n", "sort : boolean, default True\n", " Sort group keys. Get better performance by turning this off.\n", " Note this does not influence the order of observations within each\n", " group. groupby preserves the order of rows within each group.\n", "group_keys : boolean, default True\n", " When calling apply, add group keys to index to identify pieces\n", "squeeze : boolean, default False\n", " reduce the dimensionality of the return type if possible,\n", " otherwise return a consistent type\n", "\n", "Examples\n", "--------\n", "DataFrame results\n", "\n", ">>> data.groupby(func, axis=0).mean()\n", ">>> data.groupby(['col1', 'col2'])['col3'].mean()\n", "\n", "DataFrame with hierarchical index\n", "\n", ">>> data.groupby(['col1', 'col2']).mean()\n", "\n", "Returns\n", "-------\n", "GroupBy object\n", "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/pangeo/lib/python3.6/site-packages/pandas/core/generic.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d.groupby?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## simple examples\n", "### groupby one of the column" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodydatetimetimezonestatestateFIPSStateNumber...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
EFscale
028464284642846428464284642846428464284642846428464...28464284642846428464284642846428464284642846428464
120532205322053220532205322053220532205322053220532...20532205322053220532205322053220532205322053220532
29001900190019001900190019001900190019001...9001900190019001900190019001900190019001
32439243924392439243924392439243924392439...2439243924392439243924392439243924392439
4567567567567567567567567567567...567567567567567567567567567567
559595959595959595959...59595959595959595959
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6 rows × 28 columns

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" ], "text/plain": [ " om yr mo dy date time timezone state stateFIPS \\\n", "EFscale \n", "0 28464 28464 28464 28464 28464 28464 28464 28464 28464 \n", "1 20532 20532 20532 20532 20532 20532 20532 20532 20532 \n", "2 9001 9001 9001 9001 9001 9001 9001 9001 9001 \n", "3 2439 2439 2439 2439 2439 2439 2439 2439 2439 \n", "4 567 567 567 567 567 567 567 567 567 \n", "5 59 59 59 59 59 59 59 59 59 \n", "\n", " StateNumber ... lenghtmiles widthyards ns sn sg \\\n", "EFscale ... \n", "0 28464 ... 28464 28464 28464 28464 28464 \n", "1 20532 ... 20532 20532 20532 20532 20532 \n", "2 9001 ... 9001 9001 9001 9001 9001 \n", "3 2439 ... 2439 2439 2439 2439 2439 \n", "4 567 ... 567 567 567 567 567 \n", "5 59 ... 59 59 59 59 59 \n", "\n", " fips1 fips2 fips3 fips4 fc \n", "EFscale \n", "0 28464 28464 28464 28464 28464 \n", "1 20532 20532 20532 20532 20532 \n", "2 9001 9001 9001 9001 9001 \n", "3 2439 2439 2439 2439 2439 \n", "4 567 567 567 567 567 \n", "5 59 59 59 59 59 \n", "\n", "[6 rows x 28 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.groupby(by='EFscale').count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### note how all the columns reports the same value; we are counting the number of elements for each EFscale, so that makes sense" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodytimezonestateFIPSStateNumberinjuriesfatalitiesloss...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
EFscale
059448.6809651994.0921876.04749916.0117693.00189729.76405329.1873950.0288790.000843448.752269...1.03009142.1095841.0015810.9984541.0107.1578842.1637510.1406690.0327780.036432
149230.9334211985.9644465.97399215.8731253.00014628.99259723.5969220.3402980.0112022579.591146...3.20389297.5408141.0065260.9936201.0102.9031278.3344051.2579390.2558930.036139
228488.7268081978.9670045.84135115.8384623.00100029.24152923.0805471.7531390.0657704854.223861...6.965334177.7211421.0138870.9863351.0107.15253916.9777803.7413621.2291970.002889
330467.7351371979.8560895.62976615.7839283.00000028.68183722.5965569.4903650.53054529044.896049...14.906080365.6879871.0467400.9540801.0103.47437535.29069310.1705623.7679380.021320
430497.1798941978.9647275.15873015.2433863.00000028.44268121.07407460.9118174.1587301781.613437...27.523580588.8606701.1146380.8888891.091.80599648.10405623.0493838.5696650.010582
539081.1525421978.0677975.03389814.2203393.00000028.66101725.423729219.59322022.830508135.765763...39.007797839.0677971.2372880.7796611.093.15254239.06779725.47457617.7966100.000000
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6 rows × 25 columns

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" ], "text/plain": [ " om yr mo dy timezone stateFIPS \\\n", "EFscale \n", "0 59448.680965 1994.092187 6.047499 16.011769 3.001897 29.764053 \n", "1 49230.933421 1985.964446 5.973992 15.873125 3.000146 28.992597 \n", "2 28488.726808 1978.967004 5.841351 15.838462 3.001000 29.241529 \n", "3 30467.735137 1979.856089 5.629766 15.783928 3.000000 28.681837 \n", "4 30497.179894 1978.964727 5.158730 15.243386 3.000000 28.442681 \n", "5 39081.152542 1978.067797 5.033898 14.220339 3.000000 28.661017 \n", "\n", " StateNumber injuries fatalities loss ... \\\n", "EFscale ... \n", "0 29.187395 0.028879 0.000843 448.752269 ... \n", "1 23.596922 0.340298 0.011202 2579.591146 ... \n", "2 23.080547 1.753139 0.065770 4854.223861 ... \n", "3 22.596556 9.490365 0.530545 29044.896049 ... \n", "4 21.074074 60.911817 4.158730 1781.613437 ... \n", "5 25.423729 219.593220 22.830508 135.765763 ... \n", "\n", " lenghtmiles widthyards ns sn sg fips1 \\\n", "EFscale \n", "0 1.030091 42.109584 1.001581 0.998454 1.0 107.157884 \n", "1 3.203892 97.540814 1.006526 0.993620 1.0 102.903127 \n", "2 6.965334 177.721142 1.013887 0.986335 1.0 107.152539 \n", "3 14.906080 365.687987 1.046740 0.954080 1.0 103.474375 \n", "4 27.523580 588.860670 1.114638 0.888889 1.0 91.805996 \n", "5 39.007797 839.067797 1.237288 0.779661 1.0 93.152542 \n", "\n", " fips2 fips3 fips4 fc \n", "EFscale \n", "0 2.163751 0.140669 0.032778 0.036432 \n", "1 8.334405 1.257939 0.255893 0.036139 \n", "2 16.977780 3.741362 1.229197 0.002889 \n", "3 35.290693 10.170562 3.767938 0.021320 \n", "4 48.104056 23.049383 8.569665 0.010582 \n", "5 39.067797 25.474576 17.796610 0.000000 \n", "\n", "[6 rows x 25 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.groupby('EFscale').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### in this case each column does the average of its own values, so they are different. Makes sense." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EFscale\n", "0 3\n", "1 16\n", "2 9\n", "3 25\n", "4 94\n", "5 158\n", "Name: fatalities, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.groupby('EFscale').max().fatalities" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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omyrmodydatetimetimezonestatestateFIPSStateNumber...lenghtmileswidthyardsnssnsgfips1fips2fips3fips4fc
date_time
2011-05-22 16:34:0029661620115225/22/1116:34:003MO2938...21.621600.01111459714500
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1 rows × 29 columns

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" ], "text/plain": [ " om yr mo dy date time timezone state \\\n", "date_time \n", "2011-05-22 16:34:00 296616 2011 5 22 5/22/11 16:34:00 3 MO \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "2011-05-22 16:34:00 29 38 ... 21.62 1600.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "2011-05-22 16:34:00 1 1 145 97 145 0 0 \n", "\n", "[1 rows x 29 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[d['fatalities']==158]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Because our index is a DatetimeIndex (and there are ways to transform it into that type if it doesn't happen magically when you load the file) we can use some attributes that are always available for this class " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.indexes.datetimes.DatetimeIndex" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(d.index)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['1950-01-03 11:00:00', '1950-01-03 11:55:00',\n", " '1950-01-03 16:00:00', '1950-01-13 05:25:00',\n", " '1950-01-25 19:30:00', '1950-01-25 21:00:00',\n", " '1950-01-26 18:00:00', '1950-02-11 13:10:00',\n", " '1950-02-11 13:50:00', '1950-02-11 21:00:00',\n", " ...\n", " '2016-12-25 10:51:00', '2016-12-25 10:54:00',\n", " '2016-12-25 10:58:00', '2016-12-25 11:45:00',\n", " '2016-12-25 11:50:00', '2016-12-25 12:16:00',\n", " '2016-12-25 12:24:00', '2016-12-25 13:53:00',\n", " '2016-12-26 15:08:00', '2016-12-29 02:50:00'],\n", " dtype='datetime64[ns]', name='date_time', length=61062, freq=None)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([1, 1, 1, 4, 2, 2, 3, 5, 5, 5,\n", " ...\n", " 6, 6, 6, 6, 6, 6, 6, 6, 0, 3],\n", " dtype='int64', name='date_time', length=61062)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index.weekday" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([11, 11, 16, 5, 19, 21, 18, 13, 13, 21,\n", " ...\n", " 10, 10, 10, 11, 11, 12, 12, 13, 15, 2],\n", " dtype='int64', name='date_time', length=61062)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index.hour" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950,\n", " ...\n", " 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016],\n", " dtype='int64', name='date_time', length=61062)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.index.year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### let's see what is the number of tornado per each weekday" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.groupby(d.index.weekday)['EFscale'].count().plot(kind='bar', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### what about the hour of the day" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.groupby(d.index.hour)['EFscale'].count().plot(kind='bar', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### what is the average intensity per hour of the day" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.groupby(d.index.hour)['EFscale'].mean().plot(kind='bar', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### how about the interquartile range" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.groupby(d.index.hour)['EFscale'].max().plot(kind='line', ax=ax)\n", "d.groupby(d.index.hour)['EFscale'].min().plot(kind='line', ax=ax)\n", "\n", "d.groupby(d.index.hour)['EFscale'].quantile(0.25, interpolation='midpoint').plot(kind='line', ax=ax)\n", "d.groupby(d.index.hour)['EFscale'].quantile(0.5, interpolation='midpoint').plot(kind='line', ax=ax)\n", "d.groupby(d.index.hour)['EFscale'].quantile(0.75, interpolation='midpoint').plot(kind='line', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### length of path?" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "# d.groupby(d.index.hour)['widthyards'].max().plot(kind='line', ax=ax)\n", "# d.groupby(d.index.hour)['widthyards'].min().plot(kind='line', ax=ax)\n", "\n", "d.groupby(d.index.hour)['lenghtmiles'].quantile(0.25, interpolation='midpoint').plot(kind='line', ax=ax, label='25%')\n", "d.groupby(d.index.hour)['lenghtmiles'].quantile(0.25).plot(kind='line', ax=ax, label='25%')\n", "d.groupby(d.index.hour)['lenghtmiles'].quantile(0.5, interpolation='midpoint').plot(kind='line', ax=ax, label='50%')\n", "d.groupby(d.index.hour)['lenghtmiles'].quantile(0.75, interpolation='midpoint').plot(kind='line', ax=ax, label='75%')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d.groupby(['EFscale'])['lenghtmiles'].quantile(0.5).plot(kind='line',marker='o')\n", "d.groupby(['EFscale'])['lenghtmiles'].quantile(0.1).plot(kind='line',marker='o')\n", "d.groupby(['EFscale'])['lenghtmiles'].quantile(0.9).plot(kind='line',marker='o')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "tempd = d.groupby(['EFscale'])['lenghtmiles']\n", "print(type(tempd))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EFscale\n", "0 0.2\n", "1 1.0\n", "2 3.0\n", "3 9.6\n", "4 18.9\n", "5 30.3\n", "Name: lenghtmiles, dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tempd.quantile()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.boxplot(by=['EFscale'], column='lenghtmiles', ax=ax, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What if we want to groupby by more than one column?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", 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EFscale012345
date_time
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" ], "text/plain": [ "EFscale 0 1 2 3 4 5\n", "date_time \n", "0 0.293779 0.420507 0.233871 0.043779 0.008065 NaN\n", "1 0.268930 0.463446 0.212794 0.044386 0.010444 NaN\n", "2 0.276720 0.420205 0.238653 0.061493 0.002928 NaN\n", "3 0.287781 0.398714 0.250804 0.056270 0.006431 NaN\n", "4 0.309295 0.435897 0.201923 0.048077 0.004808 NaN" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dd3 = dd2.mul(1./d.groupby(by=[d.index.hour])['om'].count(), axis=0)\n", "dd3.head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dd3.plot(kind='bar', stacked=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### and once again the same analysis but for month of the year" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "d.groupby(d.index.month)['EFscale'].count().plot(kind='bar', ax=ax)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(18,3))\n", "d.groupby(d.index.year)['EFscale'].count().plot(kind='bar', ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What else can i do on these groups?" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "groupbyobject = d.groupby('EFscale')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "groupbyobject" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iterate on groups" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-03-19 13:15:00 32 1950 3 19 3/19/50 13:15:00 3 LA \n", "1950-05-01 11:30:00 70 1950 5 1 5/1/50 11:30:00 3 LA \n", "1950-05-12 17:00:00 99 1950 5 12 5/12/50 17:00:00 3 NC \n", "1950-05-18 19:00:00 111 1950 5 18 5/18/50 19:00:00 3 KS \n", "1950-05-19 01:30:00 112 1950 5 19 5/19/50 1:30:00 3 KS \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-03-19 13:15:00 22 8 ... 18.1 27.0 1 \n", "1950-05-01 11:30:00 22 16 ... 1.0 100.0 1 \n", "1950-05-12 17:00:00 37 3 ... 1.0 200.0 1 \n", "1950-05-18 19:00:00 20 13 ... 0.1 10.0 1 \n", "1950-05-19 01:30:00 20 14 ... 0.1 10.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-03-19 13:15:00 1 1 51 75 0 0 0 \n", "1950-05-01 11:30:00 1 1 59 0 0 0 0 \n", "1950-05-12 17:00:00 1 1 183 0 0 0 0 \n", "1950-05-18 19:00:00 1 1 89 0 0 0 0 \n", "1950-05-19 01:30:00 1 1 15 0 0 0 0 \n", "\n", "[5 rows x 29 columns]\n", "1\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-01-03 16:00:00 3 1950 1 3 1/3/50 16:00:00 3 OH \n", "1950-02-12 01:15:00 13 1950 2 12 2/12/50 1:15:00 3 TX \n", "1950-02-12 11:57:00 15 1950 2 12 2/12/50 11:57:00 3 TX \n", "1950-02-12 12:00:00 17 1950 2 12 2/12/50 12:00:00 3 MS \n", "1950-02-12 23:00:00 24 1950 2 12 2/12/50 23:00:00 3 LA \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-01-03 16:00:00 39 1 ... 0.1 10.0 1 \n", "1950-02-12 01:15:00 48 7 ... 2.3 233.0 1 \n", "1950-02-12 11:57:00 48 9 ... 7.7 100.0 1 \n", "1950-02-12 12:00:00 28 2 ... 2.0 10.0 1 \n", "1950-02-12 23:00:00 22 5 ... 0.5 33.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-01-03 16:00:00 1 1 161 0 0 0 0 \n", "1950-02-12 01:15:00 1 1 143 0 0 0 0 \n", "1950-02-12 11:57:00 1 1 419 0 0 0 0 \n", "1950-02-12 12:00:00 1 1 145 0 0 0 0 \n", "1950-02-12 23:00:00 1 1 35 0 0 0 0 \n", "\n", "[5 rows x 29 columns]\n", "2\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-01-25 19:30:00 5 1950 1 25 1/25/50 19:30:00 3 MO \n", "1950-01-25 21:00:00 6 1950 1 25 1/25/50 21:00:00 3 IL \n", "1950-01-26 18:00:00 7 1950 1 26 1/26/50 18:00:00 3 TX \n", "1950-02-11 13:10:00 8 1950 2 11 2/11/50 13:10:00 3 TX \n", "1950-02-11 21:00:00 10 1950 2 11 2/11/50 21:00:00 3 TX \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-01-25 19:30:00 29 2 ... 2.3 300.0 1 \n", "1950-01-25 21:00:00 17 3 ... 0.1 100.0 1 \n", "1950-01-26 18:00:00 48 1 ... 4.7 133.0 1 \n", "1950-02-11 13:10:00 48 2 ... 9.9 400.0 1 \n", "1950-02-11 21:00:00 48 4 ... 4.6 100.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-01-25 19:30:00 1 1 93 0 0 0 0 \n", "1950-01-25 21:00:00 1 1 91 0 0 0 0 \n", "1950-01-26 18:00:00 1 1 47 0 0 0 0 \n", "1950-02-11 13:10:00 1 1 39 0 0 0 0 \n", "1950-02-11 21:00:00 1 1 423 0 0 0 0 \n", "\n", "[5 rows x 29 columns]\n", "3\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-01-03 11:00:00 1 1950 1 3 1/3/50 11:00:00 3 MO \n", "1950-01-03 11:55:00 2 1950 1 3 1/3/50 11:55:00 3 IL \n", "1950-01-13 05:25:00 4 1950 1 13 1/13/50 5:25:00 3 AR \n", "1950-02-11 13:50:00 9 1950 2 11 2/11/50 13:50:00 3 TX \n", "1950-02-12 12:00:00 18 1950 2 12 2/12/50 12:00:00 3 TX \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-01-03 11:00:00 29 1 ... 9.5 150.0 2 \n", "1950-01-03 11:55:00 17 2 ... 3.6 130.0 1 \n", "1950-01-13 05:25:00 5 1 ... 0.6 17.0 1 \n", "1950-02-11 13:50:00 48 3 ... 12.0 1000.0 1 \n", "1950-02-12 12:00:00 48 10 ... 1.9 50.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-01-03 11:00:00 0 1 0 0 0 0 0 \n", "1950-01-03 11:55:00 1 1 135 0 0 0 0 \n", "1950-01-13 05:25:00 1 1 113 0 0 0 0 \n", "1950-02-11 13:50:00 1 1 201 0 0 0 0 \n", "1950-02-12 12:00:00 1 1 419 0 0 0 0 \n", "\n", "[5 rows x 29 columns]\n", "4\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1950-02-12 13:00:00 20 1950 2 12 2/12/50 13:00:00 3 LA \n", "1950-04-28 18:00:00 59 1950 4 28 4/28/50 18:00:00 3 TX \n", "1950-04-28 19:05:00 60 1950 4 28 4/28/50 19:05:00 3 OK \n", "1950-05-04 23:10:00 79 1950 5 4 5/4/50 23:10:00 3 KS \n", "1950-06-08 20:10:00 132 1950 6 8 6/8/50 20:10:00 3 KS \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1950-02-12 13:00:00 22 1 ... 82.6 100.0 1 \n", "1950-04-28 18:00:00 48 11 ... 1.3 233.0 1 \n", "1950-04-28 19:05:00 40 6 ... 4.5 200.0 1 \n", "1950-05-04 23:10:00 20 3 ... 34.3 150.0 1 \n", "1950-06-08 20:10:00 20 18 ... 17.1 700.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1950-02-12 13:00:00 1 1 31 17 15 119 0 \n", "1950-04-28 18:00:00 1 1 59 0 0 0 0 \n", "1950-04-28 19:05:00 1 1 63 0 0 0 0 \n", "1950-05-04 23:10:00 1 1 9 145 0 0 0 \n", "1950-06-08 20:10:00 1 1 159 113 0 0 0 \n", "\n", "[5 rows x 29 columns]\n", "5\n", " om yr mo dy date time timezone state \\\n", "date_time \n", "1953-05-11 16:10:00 162 1953 5 11 5/11/53 16:10:00 3 TX \n", "1953-05-29 17:00:00 201 1953 5 29 5/29/53 17:00:00 3 ND \n", "1953-06-08 19:30:00 275 1953 6 8 6/8/53 19:30:00 3 MI \n", "1953-06-27 15:45:00 318 1953 6 27 6/27/53 15:45:00 3 IA \n", "1953-12-05 17:45:00 416 1953 12 5 12/5/53 17:45:00 3 MS \n", "\n", " stateFIPS StateNumber ... lenghtmiles widthyards ns \\\n", "date_time ... \n", "1953-05-11 16:10:00 48 9 ... 20.9 583.0 1 \n", "1953-05-29 17:00:00 38 2 ... 14.8 600.0 1 \n", "1953-06-08 19:30:00 26 10 ... 18.9 833.0 1 \n", "1953-06-27 15:45:00 19 20 ... 0.1 100.0 1 \n", "1953-12-05 17:45:00 28 21 ... 9.0 500.0 1 \n", "\n", " sn sg fips1 fips2 fips3 fips4 fc \n", "date_time \n", "1953-05-11 16:10:00 1 1 309 0 0 0 0 \n", "1953-05-29 17:00:00 1 1 59 29 0 0 0 \n", "1953-06-08 19:30:00 1 1 49 87 0 0 0 \n", "1953-06-27 15:45:00 1 1 1 0 0 0 0 \n", "1953-12-05 17:45:00 1 1 149 0 0 0 0 \n", "\n", "[5 rows x 29 columns]\n" ] } ], "source": [ "# Group the dataframe by regiment, and for each regiment,\n", "for name, group in d.groupby('EFscale'): \n", " # print the name of the regiment\n", " print(name)\n", " # print the data of that regiment\n", " print(group.head())\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, group in d.groupby('EFscale'): \n", " # print the name of the regiment\n", " # print the data of that regiment\n", " group.plot.hexbin('startlon', 'startlat', cmap='viridis')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use your own function with .apply()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def get_stats(group):\n", " return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean': group.mean()}" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmaxmeanmin
EFscale
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32439.0169.714.9060800.01
4567.0202.127.5235800.10
559.0202.539.0077970.10
\n", "
" ], "text/plain": [ " count max mean min\n", "EFscale \n", "0 28464.0 103.5 1.030091 0.00\n", "1 20532.0 176.4 3.203892 0.00\n", "2 9001.0 234.7 6.965334 0.06\n", "3 2439.0 169.7 14.906080 0.01\n", "4 567.0 202.1 27.523580 0.10\n", "5 59.0 202.5 39.007797 0.10" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d['lenghtmiles'].groupby(d['EFscale']).apply(get_stats).unstack()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# .resample()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### what if I want the time series on a different time resolution, 1 month? 1 hour?" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvention\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'start'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", "Convenience method for frequency conversion and resampling of time\n", "series. Object must have a datetime-like index (DatetimeIndex,\n", "PeriodIndex, or TimedeltaIndex), or pass datetime-like values\n", "to the on or level keyword.\n", "\n", "Parameters\n", "----------\n", "rule : string\n", " the offset string or object representing target conversion\n", "axis : int, optional, default 0\n", "closed : {'right', 'left'}\n", " Which side of bin interval is closed. The default is 'left'\n", " for all frequency offsets except for 'M', 'A', 'Q', 'BM',\n", " 'BA', 'BQ', and 'W' which all have a default of 'right'.\n", "label : {'right', 'left'}\n", " Which bin edge label to label bucket with. The default is 'left'\n", " for all frequency offsets except for 'M', 'A', 'Q', 'BM',\n", " 'BA', 'BQ', and 'W' which all have a default of 'right'.\n", "convention : {'start', 'end', 's', 'e'}\n", " For PeriodIndex only, controls whether to use the start or end of\n", " `rule`\n", "loffset : timedelta\n", " Adjust the resampled time labels\n", "base : int, default 0\n", " For frequencies that evenly subdivide 1 day, the \"origin\" of the\n", " aggregated intervals. For example, for '5min' frequency, base could\n", " range from 0 through 4. Defaults to 0\n", "on : string, optional\n", " For a DataFrame, column to use instead of index for resampling.\n", " Column must be datetime-like.\n", "\n", " .. versionadded:: 0.19.0\n", "\n", "level : string or int, optional\n", " For a MultiIndex, level (name or number) to use for\n", " resampling. Level must be datetime-like.\n", "\n", " .. versionadded:: 0.19.0\n", "\n", "Notes\n", "-----\n", "To learn more about the offset strings, please see `this link\n", "`__.\n", "\n", "Examples\n", "--------\n", "\n", "Start by creating a series with 9 one minute timestamps.\n", "\n", ">>> index = pd.date_range('1/1/2000', periods=9, freq='T')\n", ">>> series = pd.Series(range(9), index=index)\n", ">>> series\n", "2000-01-01 00:00:00 0\n", "2000-01-01 00:01:00 1\n", "2000-01-01 00:02:00 2\n", "2000-01-01 00:03:00 3\n", "2000-01-01 00:04:00 4\n", "2000-01-01 00:05:00 5\n", "2000-01-01 00:06:00 6\n", "2000-01-01 00:07:00 7\n", "2000-01-01 00:08:00 8\n", "Freq: T, dtype: int64\n", "\n", "Downsample the series into 3 minute bins and sum the values\n", "of the timestamps falling into a bin.\n", "\n", ">>> series.resample('3T').sum()\n", "2000-01-01 00:00:00 3\n", "2000-01-01 00:03:00 12\n", "2000-01-01 00:06:00 21\n", "Freq: 3T, dtype: int64\n", "\n", "Downsample the series into 3 minute bins as above, but label each\n", "bin using the right edge instead of the left. Please note that the\n", "value in the bucket used as the label is not included in the bucket,\n", "which it labels. For example, in the original series the\n", "bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed\n", "value in the resampled bucket with the label ``2000-01-01 00:03:00``\n", "does not include 3 (if it did, the summed value would be 6, not 3).\n", "To include this value close the right side of the bin interval as\n", "illustrated in the example below this one.\n", "\n", ">>> series.resample('3T', label='right').sum()\n", "2000-01-01 00:03:00 3\n", "2000-01-01 00:06:00 12\n", "2000-01-01 00:09:00 21\n", "Freq: 3T, dtype: int64\n", "\n", "Downsample the series into 3 minute bins as above, but close the right\n", "side of the bin interval.\n", "\n", ">>> series.resample('3T', label='right', closed='right').sum()\n", "2000-01-01 00:00:00 0\n", "2000-01-01 00:03:00 6\n", "2000-01-01 00:06:00 15\n", "2000-01-01 00:09:00 15\n", "Freq: 3T, dtype: int64\n", "\n", "Upsample the series into 30 second bins.\n", "\n", ">>> series.resample('30S').asfreq()[0:5] #select first 5 rows\n", "2000-01-01 00:00:00 0.0\n", "2000-01-01 00:00:30 NaN\n", "2000-01-01 00:01:00 1.0\n", "2000-01-01 00:01:30 NaN\n", "2000-01-01 00:02:00 2.0\n", "Freq: 30S, dtype: float64\n", "\n", "Upsample the series into 30 second bins and fill the ``NaN``\n", "values using the ``pad`` method.\n", "\n", ">>> series.resample('30S').pad()[0:5]\n", "2000-01-01 00:00:00 0\n", "2000-01-01 00:00:30 0\n", "2000-01-01 00:01:00 1\n", "2000-01-01 00:01:30 1\n", "2000-01-01 00:02:00 2\n", "Freq: 30S, dtype: int64\n", "\n", "Upsample the series into 30 second bins and fill the\n", "``NaN`` values using the ``bfill`` method.\n", "\n", ">>> series.resample('30S').bfill()[0:5]\n", "2000-01-01 00:00:00 0\n", "2000-01-01 00:00:30 1\n", "2000-01-01 00:01:00 1\n", "2000-01-01 00:01:30 2\n", "2000-01-01 00:02:00 2\n", "Freq: 30S, dtype: int64\n", "\n", "Pass a custom function via ``apply``\n", "\n", ">>> def custom_resampler(array_like):\n", "... return np.sum(array_like)+5\n", "\n", ">>> series.resample('3T').apply(custom_resampler)\n", "2000-01-01 00:00:00 8\n", "2000-01-01 00:03:00 17\n", "2000-01-01 00:06:00 26\n", "Freq: 3T, dtype: int64\n", "\n", "For a Series with a PeriodIndex, the keyword `convention` can be\n", "used to control whether to use the start or end of `rule`.\n", "\n", ">>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',\n", " freq='A',\n", " periods=2))\n", ">>> s\n", "2012 1\n", "2013 2\n", "Freq: A-DEC, dtype: int64\n", "\n", "Resample by month using 'start' `convention`. Values are assigned to\n", "the first month of the period.\n", "\n", ">>> s.resample('M', convention='start').asfreq().head()\n", "2012-01 1.0\n", "2012-02 NaN\n", "2012-03 NaN\n", "2012-04 NaN\n", "2012-05 NaN\n", "Freq: M, dtype: float64\n", "\n", "Resample by month using 'end' `convention`. Values are assigned to\n", "the last month of the period.\n", "\n", ">>> s.resample('M', convention='end').asfreq()\n", "2012-12 1.0\n", "2013-01 NaN\n", "2013-02 NaN\n", "2013-03 NaN\n", "2013-04 NaN\n", "2013-05 NaN\n", "2013-06 NaN\n", "2013-07 NaN\n", "2013-08 NaN\n", "2013-09 NaN\n", "2013-10 NaN\n", "2013-11 NaN\n", "2013-12 2.0\n", "Freq: M, dtype: float64\n", "\n", "For DataFrame objects, the keyword ``on`` can be used to specify the\n", "column instead of the index for resampling.\n", "\n", ">>> df = pd.DataFrame(data=9*[range(4)], columns=['a', 'b', 'c', 'd'])\n", ">>> df['time'] = pd.date_range('1/1/2000', periods=9, freq='T')\n", ">>> df.resample('3T', on='time').sum()\n", " a b c d\n", "time\n", "2000-01-01 00:00:00 0 3 6 9\n", "2000-01-01 00:03:00 0 3 6 9\n", "2000-01-01 00:06:00 0 3 6 9\n", "\n", "For a DataFrame with MultiIndex, the keyword ``level`` can be used to\n", "specify on level the resampling needs to take place.\n", "\n", ">>> time = pd.date_range('1/1/2000', periods=5, freq='T')\n", ">>> df2 = pd.DataFrame(data=10*[range(4)],\n", " columns=['a', 'b', 'c', 'd'],\n", " index=pd.MultiIndex.from_product([time, [1, 2]])\n", " )\n", ">>> df2.resample('3T', level=0).sum()\n", " a b c d\n", "2000-01-01 00:00:00 0 6 12 18\n", "2000-01-01 00:03:00 0 4 8 12\n", "\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/pangeo/lib/python3.6/site-packages/pandas/core/generic.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d.resample?" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(20,3))\n", "d.resample('1m')['om'].count().plot()\n", "d.resample('1M')['om'].count().plot()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "date_time\n", "1950-01-01 7\n", "1950-02-01 20\n", "1950-03-01 21\n", "1950-04-01 15\n", "1950-05-01 61\n", "Freq: MS, Name: om, dtype: int64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.resample('1MS')['om'].count().head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "date_time\n", "1950-01-31 7\n", "1950-02-28 20\n", "1950-03-31 21\n", "1950-04-30 15\n", "1950-05-31 61\n", "Freq: M, Name: om, dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.resample('1M')['om'].count().head()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(20,3))\n", "d.resample('1QS')['om'].count().plot()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "date_time\n", "1950-01-01 24.5\n", "1950-04-01 100.5\n", "1950-07-01 172.0\n", "1950-10-01 196.5\n", "1951-01-01 9.5\n", "Freq: QS-JAN, Name: om, dtype: float64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.resample('1QS')['om'].mean().head()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "date_time\n", "1949-12-01 14.00\n", "1950-03-01 76.00\n", "1950-06-01 156.50\n", "1950-09-01 193.00\n", "1950-12-01 54.75\n", "Freq: QS-DEC, Name: om, dtype: float64" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.resample('1QS-DEC')['om'].mean().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Xarray" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "step = 1.\n", "to_bin = lambda x: np.round(x / step) * step\n", "d[\"lat1\"] = d.startlat.map(to_bin)\n", "d[\"lon1\"] = d.startlon.map(to_bin)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def to_bin2(x):\n", " return np.round(x / step) * step\n", "d[\"lat2\"] = d.startlat.map(to_bin2)\n", "d[\"lon2\"] = d.startlon.map(to_bin2)\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "TD = d[(d['yr']>=2010)&(d['yr']<=2012)].groupby(\n", " (\"lat1\", \"lon1\",pd.Grouper(freq='3H'))).endlon.count()\n", "\n" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "lat1 lon1 date_time \n", "18.0 -67.0 2011-08-07 09:00:00 1\n", " 2012-09-13 12:00:00 1\n", "21.0 -158.0 2012-03-09 09:00:00 1\n", " -156.0 2011-02-11 18:00:00 1\n", "25.0 -80.0 2010-04-26 09:00:00 1\n", "Name: endlon, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TD.head()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(TD)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "TD = pd.DataFrame(TD)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "dsT = xr.Dataset(TD)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Dimensions: (dim_0: 2476)\n", "Coordinates:\n", " * dim_0 (dim_0) MultiIndex\n", " - lat1 (dim_0) float64 18.0 18.0 21.0 21.0 25.0 25.0 26.0 26.0 26.0 ...\n", " - lon1 (dim_0) float64 -67.0 -67.0 -158.0 -156.0 -80.0 -80.0 -98.0 ...\n", " - date_time (dim_0) datetime64[ns] 2011-08-07T09:00:00 ...\n", "Data variables:\n", " endlon (dim_0) int64 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 ..." ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dsT" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT = dsT.unstack('dim_0')\n", "dsT" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "year = dsT['date_time.year']\n", "year" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT1M = dsT.resample(date_time='1MS').sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT1M" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT1M.sel(lat1=slice(25,50),lon1=slice(-125,-60), date_time=slice('2010-01-01','2010-01-31')).endlon.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT1M.sel(lat1=slice(25,50),lon1=slice(-125,-60)).endlon[0].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### rolling()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT3r = dsT1M.rolling(date_time=3,).sum()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'dsT1M' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsT1M\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'dsT1M' is not defined" ] } ], "source": [ "dsT3r" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT3r.sel(lat1=slice(25,50),lon1=slice(-125,-60)).endlon[0].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dsT3r.sel(lat1=slice(25,50),lon1=slice(-125,-60)).endlon[3].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }