只有现在是菜鸡
2021-03-12 11:51
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已结题

pandas\__init__.py报错

这是我想爬某乎的程序

 

import requests
import json
import time
import re
import datetime
import pandas as pd


def get_data(url):
    '''
    功能:访问 url 的网页,获取网页内容并返回
    参数:
        url :目标网页的 url
    返回:目标网页的 html 内容
    '''
    headers = {
        'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.182 Safari/537.36',
    }

    try:
        r = requests.get(url, headers=headers)
        r.raise_for_status()
        return r.text

    except requests.HTTPError as e:
        print(e)
        print("HTTPError")
    except requests.RequestException as e:
        print(e)
    except:
        print("Unknown Error !")


def parse_data(html):
    '''
    功能:提取 html 页面信息中的关键信息,并整合一个数组并返回
    参数:html 根据 url 获取到的网页内容
    返回:存储有 html 中提取出的关键信息的数组
    '''
    json_data = json.loads(html)['data']
    comments = []

    try:
        for item in json_data:
            comment = []
            comment.append(item['author']['name'])  # 姓名
            comment.append(item['author']['gender'])  # 性别
            # comment.append(item['author']['url'])     # 个人主页
            comment.append(item['voteup_count'])  # 点赞数
            comment.append(item['comment_count'])  # 评论数
            # comment.append(item['url'])               # 回答链接
            comments.append(comment)

        return comments

    except Exception as e:
        print(comment)
        print(e)


def save_data(comments):
    '''
    功能:将comments中的信息输出到文件中/或数据库中。
    参数:comments 将要保存的数据
    '''
    filename = 'Data/comments.csv'

    dataframe = pd.DataFrame(comments)

    dataframe.to_csv(filename, mode='a', index=False, sep=',', header=False)
    # dataframe.to_csv(filename, mode='a', index=False, sep=',', header=['name','gender','user_url','voteup','cmt_count','url'])


def main():
    url = 'https://www.zhihu.com/api/v4/questions/440710739/answers?include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Cis_labeled%2Cpaid_info%2Cpaid_info_content%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_recognized%3Bdata%5B%2A%5D.mark_infos%5B%2A%5D.url%3Bdata%5B%2A%5D.author.follower_count%2Cbadge%5B%2A%5D.topics%3Bdata%5B%2A%5D.settings.table_of_content.enabled&limit=3&offset=3&platform=desktop&sort_by=default'

    # get total cmts number
    html = get_data(url)
    totals = json.loads(html)['paging']['totals']

    print(totals)
    print('---' * 10)

    page = 0

    while (page < totals):
        url = 'https://www.zhihu.com/api/v4/questions/440710739/answers?include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Cis_labeled%2Cpaid_info%2Cpaid_info_content%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_recognized%3Bdata%5B%2A%5D.mark_infos%5B%2A%5D.url%3Bdata%5B%2A%5D.author.follower_count%2Cbadge%5B%2A%5D.topics%3Bdata%5B%2A%5D.settings.table_of_content.enabled&limit=3&offset=' + str(
            page) + '&platform=desktop&sort_by=default'

        html = get_data(url)
        comments = parse_data(html)
        save_data(comments)

        print(page)
        page += 3


if __name__ == '__main__':
    main()
    print("完成!!")

#以下是我的报错


Traceback (most recent call last):
  File "E:\pythonlearn\learn\learn.py", line 6, in <module>
    import pandas as pd
  File "D:\python3.9.0a4(64-bit)\lib\site-packages\pandas\__init__.py", line 16, in <module>
    raise ImportError(
ImportError: Unable to import required dependencies:
numpy: DLL load failed while importing mtrand: 找不到指定的程序。

 

下面是__init__py

# flake8: noqa

__docformat__ = "restructuredtext"

# Let users know if they're missing any of our hard dependencies
hard_dependencies = ("numpy", "pytz", "dateutil")
missing_dependencies = []

for dependency in hard_dependencies:
    try:
        __import__(dependency)
    except ImportError as e:
        missing_dependencies.append(f"{dependency}: {e}")

if missing_dependencies:
    raise ImportError(
        "Unable to import required dependencies:\n" + "\n".join(missing_dependencies)
    )
del hard_dependencies, dependency, missing_dependencies

# numpy compat
from pandas.compat.numpy import (
    np_version_under1p17 as _np_version_under1p17,
    np_version_under1p18 as _np_version_under1p18,
    is_numpy_dev as _is_numpy_dev,
)

try:
    from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
except ImportError as e:  # pragma: no cover
    # hack but overkill to use re
    module = str(e).replace("cannot import name ", "")
    raise ImportError(
        f"C extension: {module} not built. If you want to import "
        "pandas from the source directory, you may need to run "
        "'python setup.py build_ext --force' to build the C extensions first."
    ) from e

from pandas._config import (
    get_option,
    set_option,
    reset_option,
    describe_option,
    option_context,
    options,
)

# let init-time option registration happen
import pandas.core.config_init

from pandas.core.api import (
    # dtype
    Int8Dtype,
    Int16Dtype,
    Int32Dtype,
    Int64Dtype,
    UInt8Dtype,
    UInt16Dtype,
    UInt32Dtype,
    UInt64Dtype,
    Float32Dtype,
    Float64Dtype,
    CategoricalDtype,
    PeriodDtype,
    IntervalDtype,
    DatetimeTZDtype,
    StringDtype,
    BooleanDtype,
    # missing
    NA,
    isna,
    isnull,
    notna,
    notnull,
    # indexes
    Index,
    CategoricalIndex,
    Int64Index,
    UInt64Index,
    RangeIndex,
    Float64Index,
    MultiIndex,
    IntervalIndex,
    TimedeltaIndex,
    DatetimeIndex,
    PeriodIndex,
    IndexSlice,
    # tseries
    NaT,
    Period,
    period_range,
    Timedelta,
    timedelta_range,
    Timestamp,
    date_range,
    bdate_range,
    Interval,
    interval_range,
    DateOffset,
    # conversion
    to_numeric,
    to_datetime,
    to_timedelta,
    # misc
    Flags,
    Grouper,
    factorize,
    unique,
    value_counts,
    NamedAgg,
    array,
    Categorical,
    set_eng_float_format,
    Series,
    DataFrame,
)

from pandas.core.arrays.sparse import SparseDtype

from pandas.tseries.api import infer_freq
from pandas.tseries import offsets

from pandas.core.computation.api import eval

from pandas.core.reshape.api import (
    concat,
    lreshape,
    melt,
    wide_to_long,
    merge,
    merge_asof,
    merge_ordered,
    crosstab,
    pivot,
    pivot_table,
    get_dummies,
    cut,
    qcut,
)

import pandas.api
from pandas.util._print_versions import show_versions

from pandas.io.api import (
    # excel
    ExcelFile,
    ExcelWriter,
    read_excel,
    # parsers
    read_csv,
    read_fwf,
    read_table,
    # pickle
    read_pickle,
    to_pickle,
    # pytables
    HDFStore,
    read_hdf,
    # sql
    read_sql,
    read_sql_query,
    read_sql_table,
    # misc
    read_clipboard,
    read_parquet,
    read_orc,
    read_feather,
    read_gbq,
    read_html,
    read_json,
    read_stata,
    read_sas,
    read_spss,
)

from pandas.io.json import _json_normalize as json_normalize

from pandas.util._tester import test
import pandas.testing
import pandas.arrays

# use the closest tagged version if possible
from ._version import get_versions

v = get_versions()
__version__ = v.get("closest-tag", v["version"])
__git_version__ = v.get("full-revisionid")
del get_versions, v


# GH 27101
def __getattr__(name):
    import warnings

    if name == "datetime":
        warnings.warn(
            "The pandas.datetime class is deprecated "
            "and will be removed from pandas in a future version. "
            "Import from datetime module instead.",
            FutureWarning,
            stacklevel=2,
        )

        from datetime import datetime as dt

        return dt

    elif name == "np":

        warnings.warn(
            "The pandas.np module is deprecated "
            "and will be removed from pandas in a future version. "
            "Import numpy directly instead",
            FutureWarning,
            stacklevel=2,
        )
        import numpy as np

        return np

    elif name in {"SparseSeries", "SparseDataFrame"}:
        warnings.warn(
            f"The {name} class is removed from pandas. Accessing it from "
            "the top-level namespace will also be removed in the next version",
            FutureWarning,
            stacklevel=2,
        )

        return type(name, (), {})

    elif name == "SparseArray":

        warnings.warn(
            "The pandas.SparseArray class is deprecated "
            "and will be removed from pandas in a future version. "
            "Use pandas.arrays.SparseArray instead.",
            FutureWarning,
            stacklevel=2,
        )
        from pandas.core.arrays.sparse import SparseArray as _SparseArray

        return _SparseArray

    raise AttributeError(f"module 'pandas' has no attribute '{name}'")


# module level doc-string
__doc__ = """
pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.

Main Features
-------------
Here are just a few of the things that pandas does well:

  - Easy handling of missing data in floating point as well as non-floating
    point data.
  - Size mutability: columns can be inserted and deleted from DataFrame and
    higher dimensional objects
  - Automatic and explicit data alignment: objects can be explicitly aligned
    to a set of labels, or the user can simply ignore the labels and let
    `Series`, `DataFrame`, etc. automatically align the data for you in
    computations.
  - Powerful, flexible group by functionality to perform split-apply-combine
    operations on data sets, for both aggregating and transforming data.
  - Make it easy to convert ragged, differently-indexed data in other Python
    and NumPy data structures into DataFrame objects.
  - Intelligent label-based slicing, fancy indexing, and subsetting of large
    data sets.
  - Intuitive merging and joining data sets.
  - Flexible reshaping and pivoting of data sets.
  - Hierarchical labeling of axes (possible to have multiple labels per tick).
  - Robust IO tools for loading data from flat files (CSV and delimited),
    Excel files, databases, and saving/loading data from the ultrafast HDF5
    format.
  - Time series-specific functionality: date range generation and frequency
    conversion, moving window statistics, date shifting and lagging.
"""
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1条回答 默认 最新

  • Berrywxn 2021-06-29 22:14
    已采纳

    【简单叙述】你装的是python3.9,我也是原来装的python3.9,在import pandas时,就没有用,都出现你这个问题。

    【解决办法】卸载python3.9,装回python3.7,就不再出现这个问题。希望能帮到你!

    Traceback (most recent call last):
      File "E:\pythonlearn\learn\learn.py", line 6, in <module>
        import pandas as pd
      File "D:\python3.9.0a4(64-bit)\lib\site-packages\pandas\__init__.py", line 16, in <module>
        raise ImportError(
    ImportError: Unable to import required dependencies:
    numpy: DLL load failed while importing mtrand: 找不到指定的程序。
    
    点赞 1 评论

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