Cris 的 Python 数据分析笔记 05：Pandas 数据读取，索引，切片，计算，列整合，过滤，最值
2024-04-09 01:28:29
Pandas 数据读取，索引，切片，计算，列整合，过滤，最值
文章目录
- Pandas 数据读取，索引，切片，计算，列整合，过滤，最值
- 1. read_csv 函数
- 2. DataFrame 数据结构的常用属性
- 2. Pandas 取数据
- 3. Pandas 数据切片
- 4. 按列取值(很重要)
- 5. 按列过滤
- 6. 简单列数据处理
- 7. 类组合并添加到原 DataFrame
- 8. 最值计算
1. read_csv 函数
import pandas as pd'''xxx.csv 文件就是以 ， 分割的二维数据在 Pandas 中，核心数据结构就是 DataFrame，类似于 NumPy 的 Ndaaray（矩阵）DataFrame 数据的 dtypes 属性可以显示 .csv 文件每一列数据的数据类型，在 Pandas 中，整数就是 int64 类型；小数就是 float64 类型；字符串就是 object 类型read_csv 函数很重要哦！！！
'''
data = pd.read_csv('food_info.csv')
print(type(data))
print(data.dtypes)
print(help(pd.read_csv))<class 'pandas.core.frame.DataFrame'>
NDB_No int64
Shrt_Desc object
Water_(g) float64
Energ_Kcal int64
Protein_(g) float64
Lipid_Tot_(g) float64
Ash_(g) float64
Carbohydrt_(g) float64
Fiber_TD_(g) float64
Sugar_Tot_(g) float64
Calcium_(mg) float64
Iron_(mg) float64
Magnesium_(mg) float64
Phosphorus_(mg) float64
Potassium_(mg) float64
Sodium_(mg) float64
Zinc_(mg) float64
Copper_(mg) float64
Manganese_(mg) float64
Selenium_(mcg) float64
Vit_C_(mg) float64
Thiamin_(mg) float64
Riboflavin_(mg) float64
Niacin_(mg) float64
Vit_B6_(mg) float64
Vit_B12_(mcg) float64
Vit_A_IU float64
Vit_A_RAE float64
Vit_E_(mg) float64
Vit_D_mcg float64
Vit_D_IU float64
Vit_K_(mcg) float64
FA_Sat_(g) float64
FA_Mono_(g) float64
FA_Poly_(g) float64
Cholestrl_(mg) float64
dtype: object
Help on function read_csv in module pandas.io.parsers:read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)Read CSV (comma-separated) file into DataFrameAlso supports optionally iterating or breaking of the fileinto chunks.Additional help can be found in the `online docs for IO Tools<http://pandas.pydata.org/pandas-docs/stable/io.html>`_.Parameters----------filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any \object with a read() method (such as a file handle or StringIO)The string could be a URL. Valid URL schemes include http, ftp, s3, andfile. For file URLs, a host is expected. For instance, a local file couldbe file://localhost/path/to/table.csvsep : str, default ','Delimiter to use. If sep is None, the C engine cannot automatically detectthe separator, but the Python parsing engine can, meaning the latter willbe used and automatically detect the separator by Python's builtin sniffertool, ``csv.Sniffer``. In addition, separators longer than 1 character anddifferent from ``'\s+'`` will be interpreted as regular expressions andwill also force the use of the Python parsing engine. Note that regexdelimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``delimiter : str, default ``None``Alternative argument name for sep.delim_whitespace : boolean, default FalseSpecifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will beused as the sep. Equivalent to setting ``sep='\s+'``. If this optionis set to True, nothing should be passed in for the ``delimiter``parameter... versionadded:: 0.18.1 support for the Python parser.header : int or list of ints, default 'infer'Row number(s) to use as the column names, and the start of thedata. Default behavior is to infer the column names: if no namesare passed the behavior is identical to ``header=0`` and columnnames are inferred from the first line of the file, if columnnames are passed explicitly then the behavior is identical to``header=None``. Explicitly pass ``header=0`` to be able toreplace existing names. The header can be a list of integers thatspecify row locations for a multi-index on the columnse.g. [0,1,3]. Intervening rows that are not specified will beskipped (e.g. 2 in this example is skipped). Note that thisparameter ignores commented lines and empty lines if``skip_blank_lines=True``, so header=0 denotes the first line ofdata rather than the first line of the file.names : array-like, default NoneList of column names to use. If file contains no header row, then youshould explicitly pass header=None. Duplicates in this list will causea ``UserWarning`` to be issued.index_col : int or sequence or False, default NoneColumn to use as the row labels of the DataFrame. If a sequence is given, aMultiIndex is used. If you have a malformed file with delimiters at the endof each line, you might consider index_col=False to force pandas to _not_use the first column as the index (row names)usecols : list-like or callable, default NoneReturn a subset of the columns. If list-like, all elements must eitherbe positional (i.e. integer indices into the document columns) or stringsthat correspond to column names provided either by the user in `names` orinferred from the document header row(s). For example, a valid list-like`usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Elementorder is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.To instantiate a DataFrame from ``data`` with element order preserved use``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columnsin ``['foo', 'bar']`` order or``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``for ``['bar', 'foo']`` order.If callable, the callable function will be evaluated against the columnnames, returning names where the callable function evaluates to True. Anexample of a valid callable argument would be ``lambda x: x.upper() in['AAA', 'BBB', 'DDD']``. Using this parameter results in much fasterparsing time and lower memory usage.squeeze : boolean, default FalseIf the parsed data only contains one column then return a Seriesprefix : str, default NonePrefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...mangle_dupe_cols : boolean, default TrueDuplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than'X'...'X'. Passing in False will cause data to be overwritten if thereare duplicate names in the columns.dtype : Type name or dict of column -> type, default NoneData type for data or columns. E.g. {'a': np.float64, 'b': np.int32}Use `str` or `object` together with suitable `na_values` settingsto preserve and not interpret dtype.If converters are specified, they will be applied INSTEADof dtype conversion.engine : {'c', 'python'}, optionalParser engine to use. The C engine is faster while the python engine iscurrently more feature-complete.converters : dict, default NoneDict of functions for converting values in certain columns. Keys can eitherbe integers or column labelstrue_values : list, default NoneValues to consider as Truefalse_values : list, default NoneValues to consider as Falseskipinitialspace : boolean, default FalseSkip spaces after delimiter.skiprows : list-like or integer or callable, default NoneLine numbers to skip (0-indexed) or number of lines to skip (int)at the start of the file.If callable, the callable function will be evaluated against the rowindices, returning True if the row should be skipped and False otherwise.An example of a valid callable argument would be ``lambda x: x in [0, 2]``.skipfooter : int, default 0Number of lines at bottom of file to skip (Unsupported with engine='c')nrows : int, default NoneNumber of rows of file to read. Useful for reading pieces of large filesna_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 interpreted asNaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan','1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan','null'.keep_default_na : bool, default TrueWhether or not to include the default NaN values when parsing the data.Depending on whether `na_values` is passed in, the behavior is as follows:* If `keep_default_na` is True, and `na_values` are specified, `na_values`is appended to the default NaN values used for parsing.* If `keep_default_na` is True, and `na_values` are not specified, onlythe default NaN values are used for parsing.* If `keep_default_na` is False, and `na_values` are specified, onlythe NaN values specified `na_values` are used for parsing.* If `keep_default_na` is False, and `na_values` are not specified, nostrings will be parsed as NaN.Note that if `na_filter` is passed in as False, the `keep_default_na` and`na_values` parameters will be ignored.na_filter : boolean, default TrueDetect missing value markers (empty strings and the value of na_values). Indata without any NAs, passing na_filter=False can improve the performanceof reading a large fileverbose : boolean, default FalseIndicate number of NA values placed in non-numeric columnsskip_blank_lines : boolean, default TrueIf True, skip over blank lines rather than interpreting as NaN valuesparse_dates : boolean or list of ints or names or list of lists or dict, default False* boolean. If True -> try parsing the index.* list of ints 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. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result'foo'If a column or index contains an unparseable date, the entire column orindex will be returned unaltered as an object data type. For non-standarddatetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``Note: A fast-path exists for iso8601-formatted dates.infer_datetime_format : boolean, default FalseIf True and `parse_dates` is enabled, pandas will attempt to infer theformat of the datetime strings in the columns, and if it can be inferred,switch to a faster method of parsing them. In some cases this can increasethe parsing speed by 5-10x.keep_date_col : boolean, default FalseIf True and `parse_dates` specifies combining multiple columns thenkeep the original columns.date_parser : function, default NoneFunction to use for converting a sequence of string columns to an array ofdatetime instances. The default uses ``dateutil.parser.parser`` to do theconversion. Pandas will try to call `date_parser` in three different ways,advancing to the next if an exception occurs: 1) Pass one or more arrays(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) thestring values from the columns defined by `parse_dates` into a single arrayand pass that; and 3) call `date_parser` once for each row using one ormore strings (corresponding to the columns defined by `parse_dates`) asarguments.dayfirst : boolean, default FalseDD/MM format dates, international and European formatiterator : boolean, default FalseReturn TextFileReader object for iteration or getting chunks with``get_chunk()``.chunksize : int, default NoneReturn TextFileReader object for iteration.See the `IO Tools docs<http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_for more information on ``iterator`` and ``chunksize``.compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'For on-the-fly decompression of on-disk data. If 'infer' and`filepath_or_buffer` is path-like, then detect compression from thefollowing extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise nodecompression). If using 'zip', the ZIP file must contain only one datafile to be read in. Set to None for no decompression... versionadded:: 0.18.1 support for 'zip' and 'xz' compression.thousands : str, default NoneThousands separatordecimal : str, default '.'Character to recognize as decimal point (e.g. use ',' for European data).float_precision : string, default NoneSpecifies which converter the C engine should use for floating-pointvalues. The options are `None` for the ordinary converter,`high` for the high-precision converter, and `round_trip` for theround-trip converter.lineterminator : str (length 1), default NoneCharacter to break file into lines. Only valid with C parser.quotechar : str (length 1), optionalThe character used to denote the start and end of a quoted item. Quoteditems can include the delimiter and it will be ignored.quoting : int or csv.QUOTE_* instance, default 0Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one ofQUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).doublequote : boolean, default ``True``When quotechar is specified and quoting is not ``QUOTE_NONE``, indicatewhether or not to interpret two consecutive quotechar elements INSIDE afield as a single ``quotechar`` element.escapechar : str (length 1), default NoneOne-character string used to escape delimiter when quoting is QUOTE_NONE.comment : str, default NoneIndicates remainder of line should not be parsed. If found at the beginningof a line, the line will be ignored altogether. This parameter must be asingle character. Like empty lines (as long as ``skip_blank_lines=True``),fully commented lines are ignored by the parameter `header` but not by`skiprows`. For example, if ``comment='#'``, parsing``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' beingtreated as the header.encoding : str, default NoneEncoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Pythonstandard encodings<https://docs.python.org/3/library/codecs.html#standard-encodings>`_dialect : str or csv.Dialect instance, default NoneIf provided, this parameter will override values (default or not) for thefollowing parameters: `delimiter`, `doublequote`, `escapechar`,`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary tooverride values, a ParserWarning will be issued. See csv.Dialectdocumentation for more details.tupleize_cols : boolean, default False.. deprecated:: 0.21.0This argument will be removed and will always convert to MultiIndexLeave a list of tuples on columns as is (default is to convert toa MultiIndex on the columns)error_bad_lines : boolean, default TrueLines with too many fields (e.g. a csv line with too many commas) will bydefault cause an exception to be raised, and no DataFrame will be returned.If False, then these "bad lines" will dropped from the DataFrame that isreturned.warn_bad_lines : boolean, default TrueIf error_bad_lines is False, and warn_bad_lines is True, a warning for each"bad line" will be output.low_memory : boolean, default TrueInternally process the file in chunks, resulting in lower memory usewhile parsing, but possibly mixed type inference. To ensure no mixedtypes either set False, or specify the type with the `dtype` parameter.Note that the entire file is read into a single DataFrame regardless,use the `chunksize` or `iterator` parameter to return the data in chunks.(Only valid with C parser)memory_map : boolean, default FalseIf a filepath is provided for `filepath_or_buffer`, map the file objectdirectly onto memory and access the data directly from there. Using thisoption can improve performance because there is no longer any I/O overhead.Returns-------result : DataFrame or TextParserNone
2. DataFrame 数据结构的常用属性
# 默认显示前 5 条 csv 文件的数据，Pandas 会自动将 csv 的数据读取进来然后 jupyter notebooks 以表格的形式展现出来，十分直观
# head 函数可以使用参数，例如 head（3）表示只显示前三行的数据
data.head()# tail 函数默认显示最后 5 行数据，用于同 head 函数
data.tail()# columns 表示该 DataFrame 的列名（list 数据类型）
print(data.columns)
# shape 属性可以表示 DataFrame 数据的指标，第一个参数表示样本数量，第二个参数表示样本指标
print(data.shape)
Index(['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)','Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)','Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)','Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)','Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)','Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)','Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg','Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)','Cholestrl_(mg)'],dtype='object')
(8618, 36)
2. Pandas 取数据
# Pandas 中取数据同样很简单，直接使用 loc 函数即可
print(data.loc[0])
info = data.loc[1]
print(info)
NDB_No 1001
Shrt_Desc BUTTER WITH SALT
Water_(g) 15.87
Energ_Kcal 717
Protein_(g) 0.85
Lipid_Tot_(g) 81.11
Ash_(g) 2.11
Carbohydrt_(g) 0.06
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.06
Calcium_(mg) 24
Iron_(mg) 0.02
Magnesium_(mg) 2
Phosphorus_(mg) 24
Potassium_(mg) 24
Sodium_(mg) 643
Zinc_(mg) 0.09
Copper_(mg) 0
Manganese_(mg) 0
Selenium_(mcg) 1
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.034
Niacin_(mg) 0.042
Vit_B6_(mg) 0.003
Vit_B12_(mcg) 0.17
Vit_A_IU 2499
Vit_A_RAE 684
Vit_E_(mg) 2.32
Vit_D_mcg 1.5
Vit_D_IU 60
Vit_K_(mcg) 7
FA_Sat_(g) 51.368
FA_Mono_(g) 21.021
FA_Poly_(g) 3.043
Cholestrl_(mg) 215
Name: 0, dtype: object
NDB_No 1002
Shrt_Desc BUTTER WHIPPED WITH SALT
Water_(g) 15.87
Energ_Kcal 717
Protein_(g) 0.85
Lipid_Tot_(g) 81.11
Ash_(g) 2.11
Carbohydrt_(g) 0.06
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.06
Calcium_(mg) 24
Iron_(mg) 0.16
Magnesium_(mg) 2
Phosphorus_(mg) 23
Potassium_(mg) 26
Sodium_(mg) 659
Zinc_(mg) 0.05
Copper_(mg) 0.016
Manganese_(mg) 0.004
Selenium_(mcg) 1
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.034
Niacin_(mg) 0.042
Vit_B6_(mg) 0.003
Vit_B12_(mcg) 0.13
Vit_A_IU 2499
Vit_A_RAE 684
Vit_E_(mg) 2.32
Vit_D_mcg 1.5
Vit_D_IU 60
Vit_K_(mcg) 7
FA_Sat_(g) 50.489
FA_Mono_(g) 23.426
FA_Poly_(g) 3.012
Cholestrl_(mg) 219
Name: 1, dtype: object
3. Pandas 数据切片
# 这里的索引注意：首尾都可以取到~
info = data.loc[3:5]
info# 取任意索引位置的值，需要传入列表
index = [0,3,2]
info = data.loc[index]
info
| NDB_No | Shrt_Desc | Water_(g) | Energ_Kcal | Protein_(g) | Lipid_Tot_(g) | Ash_(g) | Carbohydrt_(g) | Fiber_TD_(g) | Sugar_Tot_(g) | ... | Vit_A_IU | Vit_A_RAE | Vit_E_(mg) | Vit_D_mcg | Vit_D_IU | Vit_K_(mcg) | FA_Sat_(g) | FA_Mono_(g) | FA_Poly_(g) | Cholestrl_(mg) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1001 | BUTTER WITH SALT | 15.87 | 717 | 0.85 | 81.11 | 2.11 | 0.06 | 0.0 | 0.06 | ... | 2499.0 | 684.0 | 2.32 | 1.5 | 60.0 | 7.0 | 51.368 | 21.021 | 3.043 | 215.0 |
| 3 | 1004 | CHEESE BLUE | 42.41 | 353 | 21.40 | 28.74 | 5.11 | 2.34 | 0.0 | 0.50 | ... | 721.0 | 198.0 | 0.25 | 0.5 | 21.0 | 2.4 | 18.669 | 7.778 | 0.800 | 75.0 |
| 2 | 1003 | BUTTER OIL ANHYDROUS | 0.24 | 876 | 0.28 | 99.48 | 0.00 | 0.00 | 0.0 | 0.00 | ... | 3069.0 | 840.0 | 2.80 | 1.8 | 73.0 | 8.6 | 61.924 | 28.732 | 3.694 | 256.0 |
3 rows × 36 columns
4. 按列取值(很重要)
# 直接可以输入列表来数据所有该列的值
info = data['NDB_No']
info
info = data[['NDB_No','Copper_(mg)']]
info
| NDB_No | Copper_(mg) | |
|---|---|---|
| 0 | 1001 | 0.000 |
| 1 | 1002 | 0.016 |
| 2 | 1003 | 0.001 |
| 3 | 1004 | 0.040 |
| 4 | 1005 | 0.024 |
| 5 | 1006 | 0.019 |
| 6 | 1007 | 0.021 |
| 7 | 1008 | 0.024 |
| 8 | 1009 | 0.056 |
| 9 | 1010 | 0.042 |
| 10 | 1011 | 0.042 |
| 11 | 1012 | 0.029 |
| 12 | 1013 | 0.040 |
| 13 | 1014 | 0.030 |
| 14 | 1015 | 0.033 |
| 15 | 1016 | 0.028 |
| 16 | 1017 | 0.019 |
| 17 | 1018 | 0.036 |
| 18 | 1019 | 0.032 |
| 19 | 1020 | 0.025 |
| 20 | 1021 | 0.080 |
| 21 | 1022 | 0.036 |
| 22 | 1023 | 0.032 |
| 23 | 1024 | 0.021 |
| 24 | 1025 | 0.032 |
| 25 | 1026 | 0.011 |
| 26 | 1027 | 0.022 |
| 27 | 1028 | 0.025 |
| 28 | 1029 | 0.034 |
| 29 | 1030 | 0.031 |
| ... | ... | ... |
| 8588 | 43544 | 0.377 |
| 8589 | 43546 | 0.040 |
| 8590 | 43550 | 0.030 |
| 8591 | 43566 | 0.116 |
| 8592 | 43570 | 0.200 |
| 8593 | 43572 | 0.545 |
| 8594 | 43585 | 0.035 |
| 8595 | 43589 | 0.027 |
| 8596 | 43595 | 0.100 |
| 8597 | 43597 | 0.027 |
| 8598 | 43598 | 0.000 |
| 8599 | 44005 | 0.000 |
| 8600 | 44018 | 0.037 |
| 8601 | 44048 | 0.026 |
| 8602 | 44055 | 0.571 |
| 8603 | 44061 | 0.838 |
| 8604 | 44074 | 0.028 |
| 8605 | 44110 | 0.023 |
| 8606 | 44158 | 0.112 |
| 8607 | 44203 | 0.020 |
| 8608 | 44258 | 0.854 |
| 8609 | 44259 | 0.040 |
| 8610 | 44260 | 0.038 |
| 8611 | 48052 | 0.182 |
| 8612 | 80200 | 0.250 |
| 8613 | 83110 | 0.100 |
| 8614 | 90240 | 0.033 |
| 8615 | 90480 | 0.020 |
| 8616 | 90560 | 0.400 |
| 8617 | 93600 | 0.250 |
8618 rows × 2 columns
5. 按列过滤
col = data.columns.tolist()
print(col)filter_col = []
for i in col:if i.endswith('(g)'):filter_col.append(i)
filter_data = data[filter_col]
print(filter_data.head(3))
['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)', 'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)', 'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)', 'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)', 'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)', 'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)', 'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg', 'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)', 'Cholestrl_(mg)']Water_(g) Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
0 15.87 0.85 81.11 2.11 0.06
1 15.87 0.85 81.11 2.11 0.06
2 0.24 0.28 99.48 0.00 0.00 Fiber_TD_(g) Sugar_Tot_(g) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g)
0 0.0 0.06 51.368 21.021 3.043
1 0.0 0.06 50.489 23.426 3.012
2 0.0 0.00 61.924 28.732 3.694
6. 简单列数据处理
# 每列数据都会按照指定的操作依次进行，例如下面的每个数据都会被 /1000
info = data['Iron_(mg)']
g_info = info/1000
print(g_info)
print(g_info[0:3])
0 0.00002
1 0.00016
2 0.00000
3 0.00031
4 0.00043
5 0.00050
6 0.00033
7 0.00064
8 0.00016
9 0.00021
10 0.00076
11 0.00007
12 0.00016
13 0.00015
14 0.00013
15 0.00014
16 0.00038
17 0.00044
18 0.00065
19 0.00023
20 0.00052
21 0.00024
22 0.00017
23 0.00013
24 0.00072
25 0.00044
26 0.00020
27 0.00022
28 0.00023
29 0.00041...
8588 0.00900
8589 0.00030
8590 0.00010
8591 0.00163
8592 0.03482
8593 0.00228
8594 0.00017
8595 0.00017
8596 0.00486
8597 0.00025
8598 0.00023
8599 0.00013
8600 0.00011
8601 0.00068
8602 0.00783
8603 0.00311
8604 0.00030
8605 0.00018
8606 0.00080
8607 0.00004
8608 0.00387
8609 0.00005
8610 0.00038
8611 0.00520
8612 0.00150
8613 0.00140
8614 0.00058
8615 0.00360
8616 0.00350
8617 0.00140
Name: Iron_(mg), Length: 8618, dtype: float64
0 0.00002
1 0.00016
2 0.00000
Name: Iron_(mg), dtype: float64
7. 类组合并添加到原 DataFrame
# 可以很方便的对列数据进行切片处理
print(data['Water_(g)'][:2])
print(data['Energ_Kcal'][:2])# 样本数量相同，很容易进行列和列之间的加成乘除操作，每列的每个元素和另外列的对应元素进行操作
info = data['Water_(g)']*data['Energ_Kcal']
print(info[:2])# 通过打印 DataFrame 的样本量和指标量来确保新添加指标成功
print(data.shape)
data['new_info'] = info
print(data.shape)
0 15.87
1 15.87
Name: Water_(g), dtype: float64
0 717
1 717
Name: Energ_Kcal, dtype: int64
0 11378.79
1 11378.79
dtype: float64
(8618, 36)
(8618, 37)
8. 最值计算
max_energ_kcal = data['Energ_Kcal'].max()
max_energ_kcal
902
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