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

Pandas 数据读取，索引，切片，计算，列整合，过滤，最值

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 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: ``'rt'``
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
``#emptyna,b,cn1,2,3`` 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 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

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

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

最后

以上就是机灵荷花为你收集整理的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，最值的全部内容，希望文章能够帮你解决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，最值所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。