Cris 的 Python 資料分析筆記 05:Pandas 資料讀取,索引,切片,計算,列整合,過濾,最值
阿新 • • 發佈:2018-11-30
Pandas 資料讀取,索引,切片,計算,列整合,過濾,最值
文章目錄
1. read_csv 函式
import pandas as pd
'''
xxx.csv 檔案就是以 , 分割的二維資料
在 Pandas 中,核心資料結構就是 DataFrame,類似於 NumPy 的 Ndaaray(矩陣)
DataFrame 資料的 dtypes 屬性可以顯示 .csv 檔案每一列資料的資料型別,在 Pandas 中,整數就是 int64 型別;
小數就是 float64 型別;字串就是 object 型別
read_csv 函式很重要哦!!!
'''
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 DataFrame Also supports optionally iterating or breaking of the file into 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, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/table.csv sep : str, default ',' Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python's builtin sniffer tool, ``csv.Sniffer``. In addition, separators longer than 1 character and different from ``'\s+'`` will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'`` delimiter : str, default ``None`` Alternative argument name for sep. delim_whitespace : boolean, default False Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be used as the sep. Equivalent to setting ``sep='\s+'``. If this option is 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 the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to ``header=0`` and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to ``header=None``. Explicitly pass ``header=0`` to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if ``skip_blank_lines=True``, so header=0 denotes the first line of data rather than the first line of the file. names : array-like, default None List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list will cause a ``UserWarning`` to be issued. index_col : int or sequence or False, default None Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of 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 None Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in `names` or inferred from the document header row(s). For example, a valid list-like `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Element order 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 columns in ``['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 column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be ``lambda x: x.upper() in ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster parsing time and lower memory usage. squeeze : boolean, default False If the parsed data only contains one column then return a Series prefix : str, default None Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : boolean, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} Use `str` or `object` together with suitable `na_values` settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : {'c', 'python'}, optional Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. converters : dict, default None Dict of functions for converting values in certain columns. Keys can either be integers or column labels true_values : list, default None Values to consider as True false_values : list, default None Values to consider as False skipinitialspace : boolean, default False Skip spaces after delimiter. skiprows : list-like or integer or callable, default None Line 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 row indices, 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 0 Number of lines at bottom of file to skip (Unsupported with engine='c') nrows : int, default None Number of rows of file to read. Useful for reading pieces of large files na_values : scalar, str, list-like, or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: '', '#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 True Whether 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, only the default NaN values are used for parsing. * If `keep_default_na` is False, and `na_values` are specified, only the NaN values specified `na_values` are used for parsing. * If `keep_default_na` is False, and `na_values` are not specified, no strings 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 True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file verbose : boolean, default False Indicate number of NA values placed in non-numeric columns skip_blank_lines : boolean, default True If True, skip over blank lines rather than interpreting as NaN values parse_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, 3 each as a separate date column. * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a 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 or index will be returned unaltered as an object data type. For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv`` Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False If True and `parse_dates` is enabled, pandas will attempt to infer the format 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 increase the parsing speed by 5-10x. keep_date_col : boolean, default False If True and `parse_dates` specifies combining multiple columns then keep the original columns. date_parser : function, default None Function to use for converting a sequence of string columns to an array of datetime instances. The default uses ``dateutil.parser.parser`` to do the conversion. 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) the string values from the columns defined by `parse_dates` into a single array and pass that; and 3) call `date_parser` once for each row using one or more strings (corresponding to the columns defined by `parse_dates`) as arguments. dayfirst : boolean, default False DD/MM format dates, international and European format iterator : boolean, default False Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. chunksize : int, default None Return 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 the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no decompression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression. thousands : str, default None Thousands separator decimal : str, default '.' Character to recognize as decimal point (e.g. use ',' for European data). float_precision : string, default None Specifies which converter the C engine should use for floating-point values. The options are `None` for the ordinary converter, `high` for the high-precision converter, and `round_trip` for the round-trip converter. lineterminator : str (length 1), default None Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored. quoting : int or csv.QUOTE_* instance, default 0 Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of QUOTE_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``, indicate whether or not to interpret two consecutive quotechar elements INSIDE a field as a single ``quotechar`` element. escapechar : str (length 1), default None One-character string used to escape delimiter when quoting is QUOTE_NONE. comment : str, default None Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single 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' being treated as the header. encoding : str, default None Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python standard encodings <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ dialect : str or csv.Dialect instance, default None If provided, this parameter will override values (default or not) for the following parameters: `delimiter`, `doublequote`, `escapechar`, `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details. tupleize_cols : boolean, default False .. deprecated:: 0.21.0 This argument will be removed and will always convert to MultiIndex Leave a list of tuples on columns as is (default is to convert to a MultiIndex on the columns) error_bad_lines : boolean, default True Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will dropped from the DataFrame that is returned. warn_bad_lines : boolean, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. low_memory : boolean, default True Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types 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 False If a filepath is provided for `filepath_or_buffer`, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead. Returns ------- result : DataFrame or TextParser None
2. DataFrame 資料結構的常用屬性
# 預設顯示前 5 條 csv 檔案的資料,Pandas 會自動將 csv 的資料讀取進來然後 jupyter notebooks 以表格的形式展現出來,十分直觀
# head 函式可以使用引數,例如 head(3)表示只顯示前三行的資料
data.head()
# tail 函式預設顯示最後 5 行資料,用於同 head 函式
data.tail()
# columns 表示該 DataFrame 的列名(list 資料型別)
print(data.columns)
# shape 屬性可以表示 DataFrame 資料的指標,第一個引數表示樣本數量,第二個引數表示樣本指標
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 取資料
# Pandas 中取資料同樣很簡單,直接使用 loc 函式即可
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 資料切片
# 這裡的索引注意:首尾都可以取到~
info = data.loc[3:5]
info
# 取任意索引位置的值,需要傳入列表
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)
# 直接可以輸入列表來資料所有該列的值
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. 按列過濾
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. 簡單列資料處理
# 每列資料都會按照指定的操作依次進行,例如下面的每個資料都會被 /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. 類組合並新增到原 DataFrame
# 可以很方便的對列資料進行切片處理
print(data['Water_(g)'][:2])
print(data['Energ_Kcal'][:2])
# 樣本數量相同,很容易進行列和列之間的加成乘除操作,每列的每個元素和另外列的對應元素進行操作
info = data['Water_(g)']*data['Energ_Kcal']
print(info[:2])
# 通過列印 DataFrame 的樣本量和指標量來確保新新增指標成功
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. 最值計算
max_energ_kcal = data['Energ_Kcal'].max()
max_energ_kcal
902