想了解使用Python读取UTF8CSV文件的新动态吗？本文将为您提供详细的信息，我们还将为您解答关于python用utf-8直接读取txt文件编码的相关问题，此外，我们还将为您介绍关于4.pytho
想了解使用Python读取UTF8 CSV文件的新动态吗？本文将为您提供详细的信息，我们还将为您解答关于python用utf-8直接读取txt文件编码的相关问题，此外，我们还将为您介绍关于4.python读写csv文件、mac中python读取csv文件编码报错问题解决、python用pd.read_csv()方法来读取csv文件、python用pd.read_csv()方法来读取csv文件的实现的新知识。
本文目录一览：- 使用Python读取UTF8 CSV文件（python用utf-8直接读取txt文件编码）
- 4.python读写csv文件
- mac中python读取csv文件编码报错问题解决
- python用pd.read_csv()方法来读取csv文件
- python用pd.read_csv()方法来读取csv文件的实现
使用Python读取UTF8 CSV文件（python用utf-8直接读取txt文件编码）
我正在尝试使用Python（仅法语和/或西班牙语字符）读取带有重音字符的CSV文件。基于csvreader的Python 2.5文档（http://docs.python.org/library/csv.html），由于csvreader仅支持ASCII，因此我想出了以下代码来读取CSV文件。
def unicode_csv_reader(unicode_csv_data, dialect=csv.excel, **kwargs): # csv.py doesn''t do Unicode; encode temporarily as UTF-8: csv_reader = csv.reader(utf_8_encoder(unicode_csv_data), dialect=dialect, **kwargs) for row in csv_reader: # decode UTF-8 back to Unicode, cell by cell: yield [unicode(cell, ''utf-8'') for cell in row]def utf_8_encoder(unicode_csv_data): for line in unicode_csv_data: yield line.encode(''utf-8'')filename = ''output.csv''reader = unicode_csv_reader(open(filename))try: products = [] for field1, field2, field3 in reader: ...以下是我尝试阅读的CSV文件的摘录：
0665000FS10120684,SD1200IS,Appareil photo numérique PowerShot de 10 Mpx de Canon avec trépied (SD1200IS) - Bleu0665000FS10120689,SD1200IS,Appareil photo numérique PowerShot de 10 Mpx de Canon avec trépied (SD1200IS) - Gris0665000FS10120687,SD1200IS,Appareil photo numérique PowerShot de 10 Mpx de Canon avec trépied (SD1200IS) - Vert...即使我尝试将编码/解码为UTF-8，我仍然收到以下异常：
Traceback (most recent call last): File ".\Test.py", line 53, in <module> for field1, field2, field3 in reader: File ".\Test.py", line 40, in unicode_csv_reader for row in csv_reader: File ".\Test.py", line 46, in utf_8_encoder yield line.encode(''utf-8'', ''ignore'')UnicodeDecodeError: ''ascii'' codec can''t decode byte 0xc3 in position 68: ordinal not in range(128)我该如何解决？
答案1
小编典典该.encode方法将应用于Unicode字符串以生成字节字符串。但是你是用字节字符串来调用它的…以错误的方式回合！查看codecs标准库中的模块，codecs.open尤其是阅读UTF-8编码文本文件的更好的通用解决方案。但是，对于csv特定的模块，你需要传递utf-8数据，而这正是你已经得到的，因此你的代码可以更加简单：
import csvdef unicode_csv_reader(utf8_data, dialect=csv.excel, **kwargs): csv_reader = csv.reader(utf8_data, dialect=dialect, **kwargs) for row in csv_reader: yield [unicode(cell, ''utf-8'') for cell in row]filename = ''da.csv''reader = unicode_csv_reader(open(filename))for field1, field2, field3 in reader: print field1, field2, field3 PS：如果事实证明你的输入数据不在utf-8中，而是在ISO-8859-1中，那么你确实需要“转码”（如果你热衷于在csv模块级别使用utf-8 ） ，形式为line.decode(''whateverweirdcodec'').encode(''utf-8'')，但你可能只需要在yield上面的代码行中使用现有编码的名称即可，而不是’utf-8’，因为csvISO-8859- *编码的字节串实际上就可以了。
4.python读写csv文件
1.爬取豆瓣top250书籍
import requests import json import csv from bs4 import BeautifulSoup books = []
def book_name(url): res = requests.get(url) html = res.text soup = BeautifulSoup(html,‘html.parser‘) items = soup.find(class_="grid-16-8 clearfix").find(class_="indent").find_all(‘table‘) for i in items: book = [] title = i.find(class_="pl2").find(‘a‘) book.append(‘《‘ + title.text.replace(‘ ‘,‘‘).replace(‘\n‘,‘‘) + ‘》‘) star = i.find(class_="star clearfix").find(class_="rating_nums") book.append(star.text + ‘分‘) try: brief = i.find(class_="quote").find(class_="inq") except AttributeError: book.append(‘”暂无简介“‘) else: book.append(brief.text) link = i.find(class_="pl2").find(‘a‘)[‘href‘] book.append(link) global books books.append(book) print(book) try: next = soup.find(class_="paginator").find(class_="next").find(‘a‘)[‘href‘] # 翻到最后一页 except TypeError: return 0 else: return next next = ‘https://book.douban.com/top250?start=0&filter=‘ count = 0 while next != 0: count += 1 next = book_name(next) print(‘-----------以上是第‘ + str(count) + ‘页的内容-----------‘) csv_file = open(‘D:/top250_books.csv‘,‘w‘,newline=‘‘,encoding=‘utf-8‘) w = csv.writer(csv_file) w.writerow([‘书名‘,‘评分‘,‘简介‘,‘链接‘]) for b in books: w.writerow(b)
结果
2.把评分为9.0的书籍保存到book_out.csv文件中
‘‘‘ 1.爬取豆瓣评分排行前250本书,保存为top250.csv 2.读取top250.csv文件，把评分为9.0以上的书籍保存到另外一个csv文件中 ‘‘‘ import csv #打开的时候必须用encoding=‘utf-8‘，否则报错 with open(‘top250.csv‘,encoding=‘utf-8‘) as rf: reader = csv.reader(rf) #读取头部 headers = next(reader) with open(‘books_out.csv‘,encoding=‘utf-8‘) as wf: writer = csv.writer(wf) #把头部信息写进去 writer.writerow(headers) for book in reader: #获取评分 score = book[1] #把评分大于9.0的过滤出来 if score and float(score) >= 9.0: writer.writerow(book)
mac中python读取csv文件编码报错问题解决
注：该文章基于mac环境。
之前在写一个简单的分班程序的时候，使用如下命令行读取csv文件,
with open(''city.csv'') as f:
lines = f.readlines()出现了报错：
‘utf-8’ codec can’t decode byte 0xb1 in position 0: invalid start byte含义为程序由于文件编码问题无法读取文件。查找了一些解决方法后终于解决，稍稍总结。
出现此种问题的原因，可能来自python程序本身或文件。一是python文件可能没有声明读取文件的编码方式，导致程序无法读取，对应解决方法一；二是文件本身的编码不是utf-8格式，导致程序无法读取，对应解决方法二。
解决方法一：在python文件中加入编码方式声明
在python文件开头加入一行编码方式声明代码，使用# -*- coding: utf-8 -*-或#code=utf-8均可。该行声明了该python程序读取文件的编码格式为utf-8。
如果是由于python程序出现的问题，此时再次运行程序，应不再报错。如仍报错，可使用方法二解决。
解决方法二：修改文件编码方式/修改程序读取方式
假设文件存放路径为/Desktop/system_code/city.csv。打开终端（在应用程序搜索“terminal”），使用cd命令查看system_code文件夹并使用vim命令打开city.csv, 代码如下：
$ cd Desktop/system_code/
$ vim city.csv此时终端会显示文件详细内容。之后使用:set命令查看文件详情:
可以看到第4行中fileencoding=latin1，说明此时文件编码方式为latin1而非utf-8。
1.修改程序读取文件时的编码方式
如果只需要程序适应这一个文件的话，直接修改程序读取文件的编码方式即可，如下。
with open(''city.csv'', encoding="latin1") as f:
lines = f.readlines()2.修改文件编码
有的时候程序需要读取多个文件，而对文件本身就要求为utf-8的格式，这时候就只能修改文件编码了。
之前在查找解决方式的时候看到了两种，第一种使用iconv命令，修改成功。第二种使用vim命令，修改后文件出现乱码。这里将两种都列出。
2.1 使用iconv命令修改
命令为：
iconv -f gbk -t utf-8 origfilename > resultfilename其中，-f后为源文件编码，-t后为转换后文件编码，origfilename为需要转码的文件，resultfilename为保存至的文件。之前我尝试了使用latin1进行转码，发现转出后为乱码，使用gbk则成功，不知道是不是因为文件内容为中文。
以我的文件为例，需要转码的文件为city_latin.csv, 希望将转码后文件保存为city_new.csv,则使用如下命令：
iconv -f gbk -t utf-8 city_latin.csv > city_new.csv之后使用vim命令查看city_new.csv的编码可看到city_new.csv为正常的utf-8编码文件。
2.2 使用vim命令修改
注：此方式在我的电脑中转换出现乱码，因此不推荐。
:set fileencoding=utf-8之后再次使用:set命令查看文件格式，可发现文件的编码格式已经被修改为utf-8:
最后使用:wq命令写入文件并退出vim即可。
python用pd.read_csv()方法来读取csv文件
import pandas as pd print("************取消第一行作为表头*************") data2 = pd.read_csv('rating.csv',header=None) print("************为各个字段取名**************") data3 = pd.read_csv('rating.csv',names=['user_id','book_id','rating']) print("***********将某一字段设为索引***************") data3 = pd.read_csv('rating.csv', names=['user_id','book_id','rating'], index_col = "user_id") print("************用sep参数设置分隔符**************") data4 = pd.read_csv('rating.csv', names=['user_id','book_id','rating'], sep=',') print("************自动补全缺失数据为NaN**************") data5 = pd.read_csv('data.csv',header=None)
查看pandas官方文档发现，read_csv读取时会自动识别表头，数据有表头时不能设置 header 为空（默认读取第一行，即 header=0)；数据无表头时，若不设置header，第一行数据会被视为表头，应传入names参数设置表头名称或设置 header=None。
read_csv(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedioBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], sep=<object object at 0x000001BBDFFF5710>, 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, skipfooter=0, 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, cache_dates=True, i@R_301_6193@tor=False, chunksize=None, compression='infer', thousands=None, decimal: str = '.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options: Union[Dict[str, Any], nonetype] = None)
Read a comma-separated values (csv) 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 <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string Could be a URL. Valid
URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
expected. A local file Could be: file://localhost/path/to/table.csv.
If you want to pass in a path object, pandas accepts any ``os.pathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
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``
Alias for sep.
header : int, list of int, 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, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
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 : bool, default False
If the parsed data only contains one column then return a Series.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, 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, optional
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
'c': 'Int64'}
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, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
true_values : list, optional
Values to consider as True.
false_values : list, optional
Values to consider as False.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
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, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
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', '<NA>', '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 : bool, 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 : bool, default False
Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int 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 cannot be represented as an array of datetimes,
say because of an unparsable value or a mixture of timezones, the 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``. To parse an index or column with a mixture of timezones,
specify ``date_parser`` to be a partially-applied
:func:`pandas.to_datetime` with ``utc=True``. See
:ref:`io.csv.mixed_timezones` for more.
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, 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 : bool, default False
If True and `parse_dates` specifies combining multiple columns then
keep the original columns.
date_parser : function, optional
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 : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.25.0
i@R_301_6193@tor : bool, default False
Return TextFileReader object for i@R_301_6193@tion or getting chunks with
``get_chunk()``.
.. versionchanged:: 1.2
``TextFileReader`` is a context manager.
chunksize : int, optional
Return TextFileReader object for i@R_301_6193@tion.
See the `IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
for more information on ``i@R_301_6193@tor`` and ``chunksize``.
.. versionchanged:: 1.2
``TextFileReader`` is a context manager.
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.
thousands : str, optional
Thousands separator.
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
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 : bool, 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), optional
One-character string used to escape other characters.
comment : str, optional
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, optional
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, optional
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.
error_bad_lines : bool, 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 : bool, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output.
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) 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.
low_memory : bool, 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 `i@R_301_6193@tor` parameter to return the data in chunks.
(Only valid with C parser).
memory_map : bool, 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.
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are ``None`` or 'high' for the ordinary converter,
'legacy' for the original lower precision pandas converter, and
'round_trip' for the round-trip converter.
.. versionchanged:: 1.2
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc., if using a URL that will
be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
will be raised if providing this argument with a non-fsspec URL.
See the fsspec and backend storage implementation docs for the set of
allowed keys and values.
.. versionadded:: 1.2
Returns
-------
DataFrame or TextParser
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
REF
https://blog.csdn.net/weixin_41855010/article/details/104287348
python用pd.read_csv()方法来读取csv文件的实现
csv文件是一种用,和换行符区分数据记录和字段的一种文件结构，可以用excel表格编辑，也可以用记事本编辑，是一种类excel的数据存储文件，也可以看成是一种数据库。pandas提供了pd.read_csv()方法可以读取其中的数据并且转换成DataFrame数据帧。python的强大之处就在于他可以把不同的数据库类型，比如txt/csv/.xls/.sql转换成统一的DataFrame格式然后进行统一的处理。真是做到了标准化。我们可以用以下代码来演示csv文件的读取操作。
import pandas as pd
data1 = pd.read_csv(''rating.csv'')
print(data1)
print("************取消第一行作为表头*************")
data2 = pd.read_csv(''rating.csv'',header=None)
print(data2)
print("************为各个字段取名**************")
data3 = pd.read_csv(''rating.csv'',names=[''user_id'',''book_id'',''rating''])
print(data3)
print("***********将某一字段设为索引***************")
data3 = pd.read_csv(''rating.csv'',
names=[''user_id'',''book_id'',''rating''],
index_col = "user_id")
print(data3)
print("************用sep参数设置分隔符**************")
data4 = pd.read_csv(''rating.csv'',
names=[''user_id'',''book_id'',''rating''],
sep='','')
print(data4)
print("************自动补全缺失数据为NaN**************")
data5 = pd.read_csv(''data.csv'',header=None)
print(data5)
输出的结果如下：
1 258 5
0 2 4081 4
1 2 260 5
2 2 9296 5
3 2 2318 3
4 2 26 4
5 2 315 3
6 2 33 4
7 2 301 5
************取消第一行作为表头*************
0 1 2
0 1 258 5
1 2 4081 4
2 2 260 5
3 2 9296 5
4 2 2318 3
5 2 26 4
6 2 315 3
7 2 33 4
8 2 301 5
************为各个字段取名**************
user_id book_id rating
0 1 258 5
1 2 4081 4
2 2 260 5
3 2 9296 5
4 2 2318 3
5 2 26 4
6 2 315 3
7 2 33 4
8 2 301 5
***********将某一字段设为索引***************
book_id rating
user_id
1 258 5
2 4081 4
2 260 5
2 9296 5
2 2318 3
2 26 4
2 315 3
2 33 4
2 301 5
************用sep参数设置分隔符**************
user_id book_id rating
0 1 258 5
1 2 4081 4
2 2 260 5
3 2 9296 5
4 2 2318 3
5 2 26 4
6 2 315 3
7 2 33 4
8 2 301 5
************自动补全缺失数据为NaN**************
0 1 2 3 4
0 1 2.0 3 4.0 5
1 6 7.0 8 NaN 10
2 11 NaN 13 14.0 15
[Finished in 4.5s]
对代码的具体解释，可以参考星号隔离bar中的注释。
到此这篇关于python用pd.read_csv()方法来读取csv文件的实现的文章就介绍到这了,更多相关python读取csv文件内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！
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