python缺失值筛选_Python练习：数据缺失值处理

import pandas as pd
help(pd.read_excel)
...
Help on function read_excel in module pandas.io.excel._base:
read_excel(io, sheet_name&＃61;0, header&＃61;0, names&＃61;None, index_col&＃61;None, usecols&＃61;None, squeeze&＃61;False, dtype&＃61;None, engine&＃61;None, converters&＃61;None, true_values&＃61;None, false_values&＃61;None, skiprows&＃61;None, nrows&＃61;None, na_values&＃61;None, keep_default_na&＃61;True, verbose&＃61;False, parse_dates&＃61;False, date_parser&＃61;None, thousands&＃61;None, comment&＃61;None, skipfooter&＃61;0, convert_float&＃61;True, mangle_dupe_cols&＃61;True, **kwds)Read an Excel file into a pandas DataFrame.Supports &＃96;xls&＃96;, &＃96;xlsx&＃96;, &＃96;xlsm&＃96;, &＃96;xlsb&＃96;, and &＃96;odf&＃96; file extensionsread from a local filesystem or URL. Supports an option to reada single sheet or a list of sheets.Parameters----------io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like objectAny valid string path is acceptable. The string could be a URL. ValidURL schemes include http, ftp, s3, and file. For file URLs, a host isexpected. A local file could be: &＃96;&＃96;file://localhost/path/to/table.xlsx&＃96;&＃96;.If you want to pass in a path object, pandas accepts any &＃96;&＃96;os.PathLike&＃96;&＃96;.By file-like object, we refer to objects with a &＃96;&＃96;read()&＃96;&＃96; method,such as a file handler (e.g. via builtin &＃96;&＃96;open&＃96;&＃96; function)or &＃96;&＃96;StringIO&＃96;&＃96;.sheet_name : str, int, list, or None, default 0Strings are used for sheet names. Integers are used in zero-indexedsheet positions. Lists of strings/integers are used to requestmultiple sheets. Specify None to get all sheets.Available cases:* Defaults to &＃96;&＃96;0&＃96;&＃96;: 1st sheet as a &＃96;DataFrame&＃96;* &＃96;&＃96;1&＃96;&＃96;: 2nd sheet as a &＃96;DataFrame&＃96;* &＃96;&＃96;"Sheet1"&＃96;&＃96;: Load sheet with name "Sheet1"* &＃96;&＃96;[0, 1, "Sheet5"]&＃96;&＃96;: Load first, second and sheet named "Sheet5"as a dict of &＃96;DataFrame&＃96;* None: All sheets.header : int, list of int, default 0Row (0-indexed) to use for the column labels of the parsedDataFrame. If a list of integers is passed those row positions willbe combined into a &＃96;&＃96;MultiIndex&＃96;&＃96;. Use None if there is no header.names : array-like, default NoneList of column names to use. If file contains no header row,then you should explicitly pass header&＃61;None.index_col : int, list of int, default NoneColumn (0-indexed) to use as the row labels of the DataFrame.Pass None if there is no such column. If a list is passed,those columns will be combined into a &＃96;&＃96;MultiIndex&＃96;&＃96;. If asubset of data is selected with &＃96;&＃96;usecols&＃96;&＃96;, index_colis based on the subset.usecols : int, str, list-like, or callable default None* If None, then parse all columns.* If str, then indicates comma separated list of Excel column lettersand column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive ofboth sides.* If list of int, then indicates list of column numbers to be parsed.* If list of string, then indicates list of column names to be parsed... versionadded:: 0.24.0* If callable, then evaluate each column name against it and parse thecolumn if the callable returns &＃96;&＃96;True&＃96;&＃96;.Returns a subset of the columns according to behavior above... versionadded:: 0.24.0squeeze : bool, default FalseIf the parsed data only contains one column then return a Series.dtype : Type name or dict of column -> type, default NoneData type for data or columns. E.g. {&＃39;a&＃39;: np.float64, &＃39;b&＃39;: np.int32}Use &＃96;object&＃96; to preserve data as stored in Excel and not interpret dtype.If converters are specified, they will be applied INSTEADof dtype conversion.engine : str, default NoneIf io is not a buffer or path, this must be set to identify io.Acceptable values are None, "xlrd", "openpyxl" or "odf".converters : dict, default NoneDict of functions for converting values in certain columns. Keys caneither be integers or column labels, values are functions that take oneinput argument, the Excel cell content, and return the transformedcontent.true_values : list, default NoneValues to consider as True.false_values : list, default NoneValues to consider as False.skiprows : list-likeRows to skip at the beginning (0-indexed).nrows : int, default NoneNumber of rows to parse... versionadded:: 0.23.0na_values : scalar, str, list-like, or dict, default NoneAdditional strings to recognize as NA/NaN. If dict passed, specificper-column NA values. By default the following values are interpretedas NaN: &＃39;&＃39;, &＃39;#N/A&＃39;, &＃39;#N/A N/A&＃39;, &＃39;#NA&＃39;, &＃39;-1.#IND&＃39;, &＃39;-1.#QNAN&＃39;, &＃39;-NaN&＃39;, &＃39;-nan&＃39;,&＃39;1.#IND&＃39;, &＃39;1.#QNAN&＃39;, &＃39;&＃39;, &＃39;N/A&＃39;, &＃39;NA&＃39;, &＃39;NULL&＃39;, &＃39;NaN&＃39;, &＃39;n/a&＃39;,&＃39;nan&＃39;, &＃39;null&＃39;.keep_default_na : bool, default TrueWhether or not to include the default NaN values when parsing the data.Depending on whether &＃96;na_values&＃96; is passed in, the behavior is as follows:* If &＃96;keep_default_na&＃96; is True, and &＃96;na_values&＃96; are specified, &＃96;na_values&＃96;is appended to the default NaN values used for parsing.* If &＃96;keep_default_na&＃96; is True, and &＃96;na_values&＃96; are not specified, onlythe default NaN values are used for parsing.* If &＃96;keep_default_na&＃96; is False, and &＃96;na_values&＃96; are specified, onlythe NaN values specified &＃96;na_values&＃96; are used for parsing.* If &＃96;keep_default_na&＃96; is False, and &＃96;na_values&＃96; are not specified, nostrings will be parsed as NaN.Note that if &＃96;na_filter&＃96; is passed in as False, the &＃96;keep_default_na&＃96; and&＃96;na_values&＃96; parameters will be ignored.na_filter : bool, default TrueDetect missing value markers (empty strings and the value of na_values). Indata without any NAs, passing na_filter&＃61;False can improve the performanceof reading a large file.verbose : bool, default FalseIndicate number of NA values placed in non-numeric columns.parse_dates : bool, list-like, or dict, default FalseThe behavior is as follows:* bool. If True -> try parsing the index.* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3each as a separate date column.* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse asa single date column.* dict, e.g. {&＃39;foo&＃39; : [1, 3]} -> parse columns 1, 3 as date and callresult &＃39;foo&＃39;If a column or index contains an unparseable date, the entire column orindex will be returned unaltered as an object data type. If you don&＃96;t want toparse some cells as date just change their type in Excel to "Text".For non-standard datetime parsing, use &＃96;&＃96;pd.to_datetime&＃96;&＃96; after &＃96;&＃96;pd.read_excel&＃96;&＃96;.Note: A fast-path exists for iso8601-formatted dates.date_parser : function, optionalFunction to use for converting a sequence of string columns to an array ofdatetime instances. The default uses &＃96;&＃96;dateutil.parser.parser&＃96;&＃96; to do theconversion. Pandas will try to call &＃96;date_parser&＃96; in three different ways,advancing to the next if an exception occurs: 1) Pass one or more arrays(as defined by &＃96;parse_dates&＃96;) as arguments; 2) concatenate (row-wise) thestring values from the columns defined by &＃96;parse_dates&＃96; into a single arrayand pass that; and 3) call &＃96;date_parser&＃96; once for each row using one ormore strings (corresponding to the columns defined by &＃96;parse_dates&＃96;) asarguments.thousands : str, default NoneThousands separator for parsing string columns to numeric. Note thatthis parameter is only necessary for columns stored as TEXT in Excel,any numeric columns will automatically be parsed, regardless of displayformat.comment : str, default NoneComments out remainder of line. Pass a character or characters to thisargument to indicate comments in the input file. Any data between thecomment string and the end of the current line is ignored.skipfooter : int, default 0Rows at the end to skip (0-indexed).convert_float : bool, default TrueConvert integral floats to int (i.e., 1.0 --> 1). If False, all numericdata will be read in as floats: Excel stores all numbers as floatsinternally.mangle_dupe_cols : bool, default TrueDuplicate columns will be specified as &＃39;X&＃39;, &＃39;X.1&＃39;, ...&＃39;X.N&＃39;, rather than&＃39;X&＃39;...&＃39;X&＃39;. Passing in False will cause data to be overwritten if thereare duplicate names in the columns.**kwds : optionalOptional keyword arguments can be passed to &＃96;&＃96;TextFileReader&＃96;&＃96;.
Returns-------DataFrame or dict of DataFramesDataFrame from the passed in Excel file. See notes in sheet_nameargument for more information on when a dict of DataFrames is returned.See Also--------to_excel : Write DataFrame to an Excel file.to_csv : Write DataFrame to a comma-separated values (csv) file.read_csv : Read a comma-separated values (csv) file into DataFrame.read_fwf : Read a table of fixed-width formatted lines into DataFrame.Examples--------The file can be read using the file name as string or an open file object:>>> pd.read_excel(&＃39;tmp.xlsx&＃39;, index_col&＃61;0) # doctest: &＃43;SKIPName Value0 string1 11 string2 22 #Comment 3>>> pd.read_excel(open(&＃39;tmp.xlsx&＃39;, &＃39;rb&＃39;),... sheet_name&＃61;&＃39;Sheet3&＃39;) # doctest: &＃43;SKIPUnnamed: 0 Name Value0 0 string1 11 1 string2 22 2 #Comment 3Index and header can be specified via the &＃96;index_col&＃96; and &＃96;header&＃96; arguments>>> pd.read_excel(&＃39;tmp.xlsx&＃39;, index_col&＃61;None, header&＃61;None) # doctest: &＃43;SKIP0 1 20 NaN Name Value1 0.0 string1 12 1.0 string2 23 2.0 #Comment 3Column types are inferred but can be explicitly specified>>> pd.read_excel(&＃39;tmp.xlsx&＃39;, index_col&＃61;0,... dtype&＃61;{&＃39;Name&＃39;: str, &＃39;Value&＃39;: float}) # doctest: &＃43;SKIPName Value0 string1 1.01 string2 2.02 #Comment 3.0True, False, and NA values, and thousands separators have defaults,but can be explicitly specified, too. Supply the values you would likeas strings or lists of strings!>>> pd.read_excel(&＃39;tmp.xlsx&＃39;, index_col&＃61;0,... na_values&＃61;[&＃39;string1&＃39;, &＃39;string2&＃39;]) # doctest: &＃43;SKIPName Value0 NaN 11 NaN 22 #Comment 3Comment lines in the excel input file can be skipped using the &＃96;comment&＃96; kwarg>>> pd.read_excel(&＃39;tmp.xlsx&＃39;, index_col&＃61;0, comment&＃61;&＃39;#&＃39;) # doctest: &＃43;SKIPName Value0 string1 1.01 string2 2.02 None NaN

import pandas as pd
... path &＃61; &＃39;C:UsersCaraDesktopstuinfo.xlsx&＃39;
... df &＃61; pd.read_excel(io &＃61; path)
... help(df.fillna)
...
Help on method fillna in module pandas.core.frame:
fillna(value&＃61;None, method&＃61;None, axis&＃61;None, inplace&＃61;False, limit&＃61;None, downcast&＃61;None) -> Union[ForwardRef(&＃39;DataFrame&＃39;), NoneType] method of pandas.core.frame.DataFrame instanceFill NA/NaN values using the specified method.Parameters----------value : scalar, dict, Series, or DataFrameValue to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use foreach index (for a Series) or column (for a DataFrame). Values notin the dict/Series/DataFrame will not be filled. This value cannotbe a list.method : {&＃39;backfill&＃39;, &＃39;bfill&＃39;, &＃39;pad&＃39;, &＃39;ffill&＃39;, None}, default NoneMethod to use for filling holes in reindexed Seriespad / ffill: propagate last valid observation forward to next validbackfill / bfill: use next valid observation to fill gap.axis : {0 or &＃39;index&＃39;, 1 or &＃39;columns&＃39;}Axis along which to fill missing values.inplace : bool, default FalseIf True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in aDataFrame).limit : int, default NoneIf method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there isa gap with more than this number of consecutive NaNs, it will onlybe partially filled. If method is not specified, this is themaximum number of entries along the entire axis where NaNs will befilled. Must be greater than 0 if not None.downcast : dict, default is NoneA dict of item->dtype of what to downcast if possible,or the string &＃39;infer&＃39; which will try to downcast to an appropriateequal type (e.g. float64 to int64 if possible).Returns-------DataFrame or NoneObject with missing values filled or None if &＃96;&＃96;inplace&＃61;True&＃96;&＃96;.See Also--------interpolate : Fill NaN values using interpolation.reindex : Conform object to new index.asfreq : Convert TimeSeries to specified frequency.Examples-------->>> df &＃61; pd.DataFrame([[np.nan, 2, np.nan, 0],... [3, 4, np.nan, 1],... [np.nan, np.nan, np.nan, 5],... [np.nan, 3, np.nan, 4]],... columns&＃61;list(&＃39;ABCD&＃39;))>>> dfA B C D0 NaN 2.0 NaN 01 3.0 4.0 NaN 12 NaN NaN NaN 53 NaN 3.0 NaN 4Replace all NaN elements with 0s.>>> df.fillna(0)A B C D0 0.0 2.0 0.0 01 3.0 4.0 0.0 12 0.0 0.0 0.0 53 0.0 3.0 0.0 4We can also propagate non-null values forward or backward.>>> df.fillna(method&＃61;&＃39;ffill&＃39;)A B C D0 NaN 2.0 NaN 01 3.0 4.0 NaN 12 3.0 4.0 NaN 53 3.0 3.0 NaN 4Replace all NaN elements in column &＃39;A&＃39;, &＃39;B&＃39;, &＃39;C&＃39;, and &＃39;D&＃39;, with 0, 1,2, and 3 respectively.>>> values &＃61; {&＃39;A&＃39;: 0, &＃39;B&＃39;: 1, &＃39;C&＃39;: 2, &＃39;D&＃39;: 3}>>> df.fillna(value&＃61;values)A B C D0 0.0 2.0 2.0 01 3.0 4.0 2.0 12 0.0 1.0 2.0 53 0.0 3.0 2.0 4Only replace the first NaN element.>>> df.fillna(value&＃61;values, limit&＃61;1)A B C D0 0.0 2.0 2.0 01 3.0 4.0 NaN 12 NaN 1.0 NaN 53 NaN 3.0 NaN 4