
import pandas as pd


file_url = "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv"


pd.read_csv(file_url)

---------------------------------------------------------------------------
ConnectionResetError                      Traceback (most recent call last)
File /usr/lib/python3.10/urllib/request.py:1348, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1347 try:
-> 1348     h.request(req.get_method(), req.selector, req.data, headers,
   1349               encode_chunked=req.has_header('Transfer-encoding'))
   1350 except OSError as err: # timeout error

File /usr/lib/python3.10/http/client.py:1282, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
   1281 """Send a complete request to the server."""
-> 1282 self._send_request(method, url, body, headers, encode_chunked)

File /usr/lib/python3.10/http/client.py:1328, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
   1327     body = _encode(body, 'body')
-> 1328 self.endheaders(body, encode_chunked=encode_chunked)

File /usr/lib/python3.10/http/client.py:1277, in HTTPConnection.endheaders(self, message_body, encode_chunked)
   1276     raise CannotSendHeader()
-> 1277 self._send_output(message_body, encode_chunked=encode_chunked)

File /usr/lib/python3.10/http/client.py:1037, in HTTPConnection._send_output(self, message_body, encode_chunked)
   1036 del self._buffer[:]
-> 1037 self.send(msg)
   1039 if message_body is not None:
   1040 
   1041     # create a consistent interface to message_body

File /usr/lib/python3.10/http/client.py:975, in HTTPConnection.send(self, data)
    974 if self.auto_open:
--> 975     self.connect()
    976 else:

File /usr/lib/python3.10/http/client.py:1454, in HTTPSConnection.connect(self)
   1452     server_hostname = self.host
-> 1454 self.sock = self._context.wrap_socket(self.sock,
   1455                                       server_hostname=server_hostname)

File /usr/lib/python3.10/ssl.py:513, in SSLContext.wrap_socket(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)
    507 def wrap_socket(self, sock, server_side=False,
    508                 do_handshake_on_connect=True,
    509                 suppress_ragged_eofs=True,
    510                 server_hostname=None, session=None):
    511     # SSLSocket class handles server_hostname encoding before it calls
    512     # ctx._wrap_socket()
--> 513     return self.sslsocket_class._create(
    514         sock=sock,
    515         server_side=server_side,
    516         do_handshake_on_connect=do_handshake_on_connect,
    517         suppress_ragged_eofs=suppress_ragged_eofs,
    518         server_hostname=server_hostname,
    519         context=self,
    520         session=session
    521     )

File /usr/lib/python3.10/ssl.py:1071, in SSLSocket._create(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)
   1070             raise ValueError("do_handshake_on_connect should not be specified for non-blocking sockets")
-> 1071         self.do_handshake()
   1072 except (OSError, ValueError):

File /usr/lib/python3.10/ssl.py:1342, in SSLSocket.do_handshake(self, block)
   1341         self.settimeout(None)
-> 1342     self._sslobj.do_handshake()
   1343 finally:

ConnectionResetError: [Errno 104] Connection reset by peer

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
Cell In[285], line 1
----> 1 pd.read_csv(file_url)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/util/_decorators.py:211, in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper(*args, **kwargs)
    209     else:
    210         kwargs[new_arg_name] = new_arg_value
--> 211 return func(*args, **kwargs)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    935 kwds_defaults = _refine_defaults_read(
    936     dialect,
    937     delimiter,
   (...)
    946     defaults={"delimiter": ","},
    947 )
    948 kwds.update(kwds_defaults)
--> 950 return _read(filepath_or_buffer, kwds)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605, in _read(filepath_or_buffer, kwds)
    602 _validate_names(kwds.get("names", None))
    604 # Create the parser.
--> 605 parser = TextFileReader(filepath_or_buffer, **kwds)
    607 if chunksize or iterator:
    608     return parser

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442, in TextFileReader.__init__(self, f, engine, **kwds)
   1439     self.options["has_index_names"] = kwds["has_index_names"]
   1441 self.handles: IOHandles | None = None
-> 1442 self._engine = self._make_engine(f, self.engine)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1735, in TextFileReader._make_engine(self, f, engine)
   1733     if "b" not in mode:
   1734         mode += "b"
-> 1735 self.handles = get_handle(
   1736     f,
   1737     mode,
   1738     encoding=self.options.get("encoding", None),
   1739     compression=self.options.get("compression", None),
   1740     memory_map=self.options.get("memory_map", False),
   1741     is_text=is_text,
   1742     errors=self.options.get("encoding_errors", "strict"),
   1743     storage_options=self.options.get("storage_options", None),
   1744 )
   1745 assert self.handles is not None
   1746 f = self.handles.handle

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:713, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    710     codecs.lookup_error(errors)
    712 # open URLs
--> 713 ioargs = _get_filepath_or_buffer(
    714     path_or_buf,
    715     encoding=encoding,
    716     compression=compression,
    717     mode=mode,
    718     storage_options=storage_options,
    719 )
    721 handle = ioargs.filepath_or_buffer
    722 handles: list[BaseBuffer]

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:363, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
    361 # assuming storage_options is to be interpreted as headers
    362 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
--> 363 with urlopen(req_info) as req:
    364     content_encoding = req.headers.get("Content-Encoding", None)
    365     if content_encoding == "gzip":
    366         # Override compression based on Content-Encoding header

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:265, in urlopen(*args, **kwargs)
    259 """
    260 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
    261 the stdlib.
    262 """
    263 import urllib.request
--> 265 return urllib.request.urlopen(*args, **kwargs)

File /usr/lib/python3.10/urllib/request.py:216, in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    214 else:
    215     opener = _opener
--> 216 return opener.open(url, data, timeout)

File /usr/lib/python3.10/urllib/request.py:519, in OpenerDirector.open(self, fullurl, data, timeout)
    516     req = meth(req)
    518 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 519 response = self._open(req, data)
    521 # post-process response
    522 meth_name = protocol+"_response"

File /usr/lib/python3.10/urllib/request.py:536, in OpenerDirector._open(self, req, data)
    533     return result
    535 protocol = req.type
--> 536 result = self._call_chain(self.handle_open, protocol, protocol +
    537                           '_open', req)
    538 if result:
    539     return result

File /usr/lib/python3.10/urllib/request.py:496, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
    494 for handler in handlers:
    495     func = getattr(handler, meth_name)
--> 496     result = func(*args)
    497     if result is not None:
    498         return result

File /usr/lib/python3.10/urllib/request.py:1391, in HTTPSHandler.https_open(self, req)
   1390 def https_open(self, req):
-> 1391     return self.do_open(http.client.HTTPSConnection, req,
   1392         context=self._context, check_hostname=self._check_hostname)

File /usr/lib/python3.10/urllib/request.py:1351, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1348         h.request(req.get_method(), req.selector, req.data, headers,
   1349                   encode_chunked=req.has_header('Transfer-encoding'))
   1350     except OSError as err: # timeout error
-> 1351         raise URLError(err)
   1352     r = h.getresponse()
   1353 except:

URLError: <urlopen error [Errno 104] Connection reset by peer>


excel_url = "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.xlsx"


pd.read_excel(excel_url)

---------------------------------------------------------------------------
ConnectionResetError                      Traceback (most recent call last)
File /usr/lib/python3.10/urllib/request.py:1348, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1347 try:
-> 1348     h.request(req.get_method(), req.selector, req.data, headers,
   1349               encode_chunked=req.has_header('Transfer-encoding'))
   1350 except OSError as err: # timeout error

File /usr/lib/python3.10/http/client.py:1282, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
   1281 """Send a complete request to the server."""
-> 1282 self._send_request(method, url, body, headers, encode_chunked)

File /usr/lib/python3.10/http/client.py:1328, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
   1327     body = _encode(body, 'body')
-> 1328 self.endheaders(body, encode_chunked=encode_chunked)

File /usr/lib/python3.10/http/client.py:1277, in HTTPConnection.endheaders(self, message_body, encode_chunked)
   1276     raise CannotSendHeader()
-> 1277 self._send_output(message_body, encode_chunked=encode_chunked)

File /usr/lib/python3.10/http/client.py:1037, in HTTPConnection._send_output(self, message_body, encode_chunked)
   1036 del self._buffer[:]
-> 1037 self.send(msg)
   1039 if message_body is not None:
   1040 
   1041     # create a consistent interface to message_body

File /usr/lib/python3.10/http/client.py:975, in HTTPConnection.send(self, data)
    974 if self.auto_open:
--> 975     self.connect()
    976 else:

File /usr/lib/python3.10/http/client.py:1454, in HTTPSConnection.connect(self)
   1452     server_hostname = self.host
-> 1454 self.sock = self._context.wrap_socket(self.sock,
   1455                                       server_hostname=server_hostname)

File /usr/lib/python3.10/ssl.py:513, in SSLContext.wrap_socket(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)
    507 def wrap_socket(self, sock, server_side=False,
    508                 do_handshake_on_connect=True,
    509                 suppress_ragged_eofs=True,
    510                 server_hostname=None, session=None):
    511     # SSLSocket class handles server_hostname encoding before it calls
    512     # ctx._wrap_socket()
--> 513     return self.sslsocket_class._create(
    514         sock=sock,
    515         server_side=server_side,
    516         do_handshake_on_connect=do_handshake_on_connect,
    517         suppress_ragged_eofs=suppress_ragged_eofs,
    518         server_hostname=server_hostname,
    519         context=self,
    520         session=session
    521     )

File /usr/lib/python3.10/ssl.py:1071, in SSLSocket._create(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)
   1070             raise ValueError("do_handshake_on_connect should not be specified for non-blocking sockets")
-> 1071         self.do_handshake()
   1072 except (OSError, ValueError):

File /usr/lib/python3.10/ssl.py:1342, in SSLSocket.do_handshake(self, block)
   1341         self.settimeout(None)
-> 1342     self._sslobj.do_handshake()
   1343 finally:

ConnectionResetError: [Errno 104] Connection reset by peer

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
Cell In[8], line 1
----> 1 pd.read_excel(excel_url)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/util/_decorators.py:211, in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper(*args, **kwargs)
    209     else:
    210         kwargs[new_arg_name] = new_arg_value
--> 211 return func(*args, **kwargs)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/excel/_base.py:482, in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)
    480 if not isinstance(io, ExcelFile):
    481     should_close = True
--> 482     io = ExcelFile(io, storage_options=storage_options, engine=engine)
    483 elif engine and engine != io.engine:
    484     raise ValueError(
    485         "Engine should not be specified when passing "
    486         "an ExcelFile - ExcelFile already has the engine set"
    487     )

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/excel/_base.py:1652, in ExcelFile.__init__(self, path_or_buffer, engine, storage_options)
   1650     ext = "xls"
   1651 else:
-> 1652     ext = inspect_excel_format(
   1653         content_or_path=path_or_buffer, storage_options=storage_options
   1654     )
   1655     if ext is None:
   1656         raise ValueError(
   1657             "Excel file format cannot be determined, you must specify "
   1658             "an engine manually."
   1659         )

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/excel/_base.py:1525, in inspect_excel_format(content_or_path, storage_options)
   1522 if isinstance(content_or_path, bytes):
   1523     content_or_path = BytesIO(content_or_path)
-> 1525 with get_handle(
   1526     content_or_path, "rb", storage_options=storage_options, is_text=False
   1527 ) as handle:
   1528     stream = handle.handle
   1529     stream.seek(0)

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:713, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    710     codecs.lookup_error(errors)
    712 # open URLs
--> 713 ioargs = _get_filepath_or_buffer(
    714     path_or_buf,
    715     encoding=encoding,
    716     compression=compression,
    717     mode=mode,
    718     storage_options=storage_options,
    719 )
    721 handle = ioargs.filepath_or_buffer
    722 handles: list[BaseBuffer]

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:363, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
    361 # assuming storage_options is to be interpreted as headers
    362 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
--> 363 with urlopen(req_info) as req:
    364     content_encoding = req.headers.get("Content-Encoding", None)
    365     if content_encoding == "gzip":
    366         # Override compression based on Content-Encoding header

File ~/usr/local/default/lib/python3.10/site-packages/pandas/io/common.py:265, in urlopen(*args, **kwargs)
    259 """
    260 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
    261 the stdlib.
    262 """
    263 import urllib.request
--> 265 return urllib.request.urlopen(*args, **kwargs)

File /usr/lib/python3.10/urllib/request.py:216, in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    214 else:
    215     opener = _opener
--> 216 return opener.open(url, data, timeout)

File /usr/lib/python3.10/urllib/request.py:519, in OpenerDirector.open(self, fullurl, data, timeout)
    516     req = meth(req)
    518 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 519 response = self._open(req, data)
    521 # post-process response
    522 meth_name = protocol+"_response"

File /usr/lib/python3.10/urllib/request.py:536, in OpenerDirector._open(self, req, data)
    533     return result
    535 protocol = req.type
--> 536 result = self._call_chain(self.handle_open, protocol, protocol +
    537                           '_open', req)
    538 if result:
    539     return result

File /usr/lib/python3.10/urllib/request.py:496, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
    494 for handler in handlers:
    495     func = getattr(handler, meth_name)
--> 496     result = func(*args)
    497     if result is not None:
    498         return result

File /usr/lib/python3.10/urllib/request.py:1391, in HTTPSHandler.https_open(self, req)
   1390 def https_open(self, req):
-> 1391     return self.do_open(http.client.HTTPSConnection, req,
   1392         context=self._context, check_hostname=self._check_hostname)

File /usr/lib/python3.10/urllib/request.py:1351, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
   1348         h.request(req.get_method(), req.selector, req.data, headers,
   1349                   encode_chunked=req.has_header('Transfer-encoding'))
   1350     except OSError as err: # timeout error
-> 1351         raise URLError(err)
   1352     r = h.getresponse()
   1353 except:

URLError: <urlopen error [Errno 104] Connection reset by peer>


pd.read_csv("wallet.csv")


wallet  = pd.read_csv("wallet.csv")


wallet


type(wallet)

pandas.core.frame.DataFrame


pd.read_excel("wallet.xlsx")


help(pd.read_csv)

Help on function read_csv in module pandas.io.parsers.readers:

read_csv(filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]', *, sep: 'str | None | lib.NoDefault' = <no_default>, delimiter: 'str | None | lib.NoDefault' = None, header: "int | Sequence[int] | None | Literal['infer']" = 'infer', names: 'Sequence[Hashable] | None | lib.NoDefault' = <no_default>, index_col: 'IndexLabel | Literal[False] | None' = None, usecols=None, squeeze: 'bool | None' = None, prefix: 'str | lib.NoDefault' = <no_default>, mangle_dupe_cols: 'bool' = True, dtype: 'DtypeArg | None' = None, engine: 'CSVEngine | None' = None, converters=None, true_values=None, false_values=None, skipinitialspace: 'bool' = False, skiprows=None, skipfooter: 'int' = 0, nrows: 'int | None' = None, na_values=None, keep_default_na: 'bool' = True, na_filter: 'bool' = True, verbose: 'bool' = False, skip_blank_lines: 'bool' = True, parse_dates=None, infer_datetime_format: 'bool' = False, keep_date_col: 'bool' = False, date_parser=None, dayfirst: 'bool' = False, cache_dates: 'bool' = True, iterator: 'bool' = False, chunksize: 'int | None' = None, compression: 'CompressionOptions' = 'infer', thousands: 'str | None' = None, decimal: 'str' = '.', lineterminator: 'str | None' = None, quotechar: 'str' = '"', quoting: 'int' = 0, doublequote: 'bool' = True, escapechar: 'str | None' = None, comment: 'str | None' = None, encoding: 'str | None' = None, encoding_errors: 'str | None' = 'strict', dialect: 'str | csv.Dialect | None' = None, error_bad_lines: 'bool | None' = None, warn_bad_lines: 'bool | None' = None, on_bad_lines=None, delim_whitespace: 'bool' = False, low_memory=True, memory_map: 'bool' = False, float_precision: "Literal['high', 'legacy'] | None" = None, storage_options: 'StorageOptions' = None) -> 'DataFrame | TextFileReader'
    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, None, 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, optional, 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). If ``names`` are given, the document
        header row(s) are not taken into account. 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.
    
        .. deprecated:: 1.4.0
            Append ``.squeeze("columns")`` to the call to ``read_csv`` to squeeze
            the data.
    prefix : str, optional
        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
    
        .. deprecated:: 1.4.0
           Use a list comprehension on the DataFrame's columns after calling ``read_csv``.
    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.
    
        .. deprecated:: 1.5.0
            Not implemented, and a new argument to specify the pattern for the
            names of duplicated columns will be added instead
    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.
    
        .. versionadded:: 1.5.0
    
            Support for defaultdict was added. Specify a defaultdict as input where
            the default determines the dtype of the columns which are not explicitly
            listed.
    engine : {'c', 'python', 'pyarrow'}, optional
        Parser engine to use. The C and pyarrow engines are faster, while the python engine
        is currently more feature-complete. Multithreading is currently only supported by
        the pyarrow engine.
    
        .. versionadded:: 1.4.0
    
            The "pyarrow" engine was added as an *experimental* engine, and some features
            are unsupported, or may not work correctly, with this engine.
    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
    iterator : bool, default False
        Return TextFileReader object for iteration or getting chunks with
        ``get_chunk()``.
    
        .. versionchanged:: 1.2
    
           ``TextFileReader`` is a context manager.
    chunksize : int, optional
        Return TextFileReader object for iteration.
        See the `IO Tools docs
        <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
        for more information on ``iterator`` and ``chunksize``.
    
        .. versionchanged:: 1.2
    
           ``TextFileReader`` is a context manager.
    compression : str or dict, 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', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
        (otherwise no compression).
        If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
        Set to ``None`` for no decompression.
        Can also be a dict with key ``'method'`` set
        to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'tar'``} and other
        key-value pairs are forwarded to
        ``zipfile.ZipFile``, ``gzip.GzipFile``,
        ``bz2.BZ2File``, ``zstandard.ZstdDecompressor`` or
        ``tarfile.TarFile``, respectively.
        As an example, the following could be passed for Zstandard decompression using a
        custom compression dictionary:
        ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
    
            .. versionadded:: 1.5.0
                Added support for `.tar` files.
    
        .. versionchanged:: 1.4.0 Zstandard support.
    
    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>`_ .
    
        .. versionchanged:: 1.2
    
           When ``encoding`` is ``None``, ``errors="replace"`` is passed to
           ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
           This behavior was previously only the case for ``engine="python"``.
    
        .. versionchanged:: 1.3.0
    
           ``encoding_errors`` is a new argument. ``encoding`` has no longer an
           influence on how encoding errors are handled.
    
    encoding_errors : str, optional, default "strict"
        How encoding errors are treated. `List of possible values
        <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
    
        .. versionadded:: 1.3.0
    
    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, optional, default ``None``
        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 be dropped from the DataFrame that is
        returned.
    
        .. deprecated:: 1.3.0
           The ``on_bad_lines`` parameter should be used instead to specify behavior upon
           encountering a bad line instead.
    warn_bad_lines : bool, optional, default ``None``
        If error_bad_lines is False, and warn_bad_lines is True, a warning for each
        "bad line" will be output.
    
        .. deprecated:: 1.3.0
           The ``on_bad_lines`` parameter should be used instead to specify behavior upon
           encountering a bad line instead.
    on_bad_lines : {'error', 'warn', 'skip'} or callable, default 'error'
        Specifies what to do upon encountering a bad line (a line with too many fields).
        Allowed values are :
    
            - 'error', raise an Exception when a bad line is encountered.
            - 'warn', raise a warning when a bad line is encountered and skip that line.
            - 'skip', skip bad lines without raising or warning when they are encountered.
    
        .. versionadded:: 1.3.0
    
        .. versionadded:: 1.4.0
    
            - callable, function with signature
              ``(bad_line: list[str]) -> list[str] | None`` that will process a single
              bad line. ``bad_line`` is a list of strings split by the ``sep``.
              If the function returns ``None``, the bad line will be ignored.
              If the function returns a new list of strings with more elements than
              expected, a ``ParserWarning`` will be emitted while dropping extra elements.
              Only supported when ``engine="python"``
    
    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 `iterator` 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. For HTTP(S) URLs the key-value pairs
        are forwarded to ``urllib.request.Request`` as header options. For other
        URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
        forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
        details, and for more examples on storage options refer `here
        <https://pandas.pydata.org/docs/user_guide/io.html?
        highlight=storage_options#reading-writing-remote-files>`_.
    
        .. versionadded:: 1.2
    
    Returns
    -------
    DataFrame or TextParser
        A comma-separated values (csv) file is returned as two-dimensional
        data structure with labeled axes.
    
    See Also
    --------
    DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
    read_csv : Read a comma-separated values (csv) file into DataFrame.
    read_fwf : Read a table of fixed-width formatted lines into DataFrame.
    
    Examples
    --------
    >>> pd.read_csv('data.csv')  # doctest: +SKIP


!cat 2ddata.txt

11,12,13
21,22,23
31,32,33


pd.read_csv("2ddata.txt", header=None)


pd.read_csv("2ddata.txt", header=None, names=["col1", "col2", "col3"])


wallet


wallet.head() # method from dataframe


wallet.head(10)


wallet.tail()


wallet.describe() # basic stats of numeric columns


moneycontrolurl = "https://www.moneycontrol.com/markets/indian-indices/"


pd.read_html(moneycontrolurl)

[      Stock Name                  Sector     LTP  Change  %Chg
 0  IndusInd Bank  Banks - Private Sector  1072.1   -72.7 -6.35,
   Stock Name   Sector      LTP   Change     %Chg
 0    No Data  No Data  No Data  No Data  No Data,
          Stock Name                             Sector       LTP  Change  %Chg
 0           Infosys               Computers - Software   1449.50  -22.05 -1.50
 1               M&M                Auto - Cars & Jeeps   1211.15  -15.55 -1.27
 2       Adani Ports           Infrastructure - General    689.70   -8.10 -1.16
 3       Tata Motors                 Auto - LCVs & HCVs    431.10   -4.75 -1.09
 4   SBI Life Insura            Life & Health Insurance   1085.05  -10.85 -0.99
 5     Hero Motocorp              Auto - 2 & 3 Wheelers   2416.60  -23.70 -0.97
 6              BPCL                         Refineries    323.15   -2.90 -0.89
 7          HCL Tech               Computers - Software   1107.25   -9.05 -0.81
 8        Coal India                  Mining & Minerals    222.60   -1.75 -0.78
 9    Dr Reddys Labs                    Pharmaceuticals   4364.00  -31.55 -0.72
 10    Maruti Suzuki                Auto - Cars & Jeeps   8552.00  -49.55 -0.58
 11        HDFC Life            Life & Health Insurance    487.90   -2.35 -0.48
 12  TATA Cons. Prod         Plantations - Tea & Coffee    701.25   -3.15 -0.45
 13            Wipro               Computers - Software    387.60   -1.50 -0.39
 14    Bajaj Finserv              Finance - Investments   1324.35   -4.50 -0.34
 15    Eicher Motors                 Auto - LCVs & HCVs   3106.55   -9.55 -0.31
 16              SBI              Banks - Public Sector    545.75   -1.60 -0.29
 17           Nestle                    Food Processing  18432.10  -53.65 -0.29
 18              TCS               Computers - Software   3321.55   -9.45 -0.28
 19       Divis Labs                    Pharmaceuticals   2780.00   -7.50 -0.27
 20    Titan Company                      Miscellaneous   2369.30   -6.20 -0.26
 21              ITC                        Diversified    387.00   -1.00 -0.26
 22       Sun Pharma                    Pharmaceuticals    953.25   -2.30 -0.24
 23       Tata Steel                       Iron & Steel    107.85   -0.25 -0.23
 24        Axis Bank             Banks - Private Sector    850.85   -1.05 -0.12
 25             NTPC  Power - Generation & Distribution    180.55   -0.20 -0.11
 26       Bajaj Auto              Auto - 2 & 3 Wheelers   3819.25   -2.65 -0.07,
          Stock Name                             Sector      LTP  Change  %Chg
 0   Adani Enterpris                            Trading  1925.95   29.75  1.57
 1        ICICI Bank             Banks - Private Sector   852.40    9.75  1.16
 2   Apollo Hospital       Hospitals & Medical Services  4356.60   39.35  0.91
 3            Grasim                        Diversified  1608.20   12.30  0.77
 4         JSW Steel                      Steel - Large   683.80    4.40  0.65
 5              ONGC       Oil Drilling And Exploration   156.80    1.00  0.64
 6   Power Grid Corp  Power - Generation & Distribution   227.85    1.30  0.57
 7             Cipla                    Pharmaceuticals   886.15    4.95  0.56
 8         HDFC Bank             Banks - Private Sector  1597.05    8.40  0.53
 9               HUL                      Personal Care  2478.15   12.70  0.52
 10        Britannia                    Food Processing  4331.30   20.30  0.47
 11   Kotak Mahindra             Banks - Private Sector  1707.00    7.70  0.45
 12         Reliance                         Refineries  2331.65    8.95  0.39
 13    Bajaj Finance                     Finance - NBFC  5897.20   23.05  0.39
 14             HDFC                  Finance - Housing  2618.30    9.40  0.36
 15         Hindalco                       Iron & Steel   406.90    1.25  0.31
 16              UPL                          Chemicals   712.55    0.90  0.13
 17           Larsen           Infrastructure - General  2160.15    2.30  0.11
 18    Bharti Airtel       Telecommunications - Service   774.35    0.60  0.08
 19  UltraTechCement                     Cement - Major  7226.60    0.20  0.00
 20     Asian Paints                 Paints & Varnishes  2830.10   -0.10  0.00,
   Stock Name   Sector      LTP   Change     %Chg
 0    No Data  No Data  No Data  No Data  No Data,
       Stock Name                Sector      LTP  Change  %Chg
 0  Tech Mahindra  Computers - Software  1141.05    79.8  7.52]


data = pd.read_html(moneycontrolurl)


type(data)

list


len(data)

6


data[0]


data[1]


data[2]


data[3]


wallet.columns # this has names of columns

Index(['Unnamed: 0', 'date', 'category', 'description', 'debit'], dtype='object')


!head wallet.csv

,date,category,description,debit
0,2021-03-07 14:53:28.377359,Music,Amazon,421.2073272347991
1,2020-10-08 09:53:28.377359,Food,Swiggy,328.4400802428426
2,2021-02-23 09:53:28.377359,Books,Amazon,244.67943701511354
3,2020-11-01 14:53:28.377359,Utility,Phone,222.7563175805277
4,2021-06-05 13:53:28.377359,Books,Flipcart,494.1284923793595
5,2021-07-28 19:53:28.377359,Utility,Electricity,219.94171130968408
6,2021-04-16 11:53:28.377359,Books,Amazon Kindle,270.32259514795845
7,2021-02-15 10:53:28.377359,Food,Zomato,457.1831036346536
8,2021-08-10 19:53:28.377359,Utility,Phone,151.49637259947792


wallet.debit # access column like an attribute inside the class

0     421.207327
1     328.440080
2     244.679437
3     222.756318
4     494.128492
         ...    
95    388.671213
96    467.554562
97    320.789434
98    442.096469
99    100.455501
Name: debit, Length: 100, dtype: float64


type(wallet.debit)

pandas.core.series.Series


wallet['debit'] # can access columns in dictionary fashion

0     421.207327
1     328.440080
2     244.679437
3     222.756318
4     494.128492
         ...    
95    388.671213
96    467.554562
97    320.789434
98    442.096469
99    100.455501
Name: debit, Length: 100, dtype: float64


wallet.columns #just names of columns in this dataframe.. it like a list

Index(['Unnamed: 0', 'date', 'category', 'description', 'debit'], dtype='object')


wallet.columns[0]

'Unnamed: 0'


wallet.Unnamed: 0 # this is not possible because of space (special char)


wallet['Unnamed: 0']

0      0
1      1
2      2
3      3
4      4
      ..
95    95
96    96
97    97
98    98
99    99
Name: Unnamed: 0, Length: 100, dtype: int64


wallet.debit

0     421.207327
1     328.440080
2     244.679437
3     222.756318
4     494.128492
         ...    
95    388.671213
96    467.554562
97    320.789434
98    442.096469
99    100.455501
Name: debit, Length: 100, dtype: float64


wallet.debit.sum()

31059.590543177277


wallet.debit.mean()

310.5959054317728


wallet.debit.describe()

count    100.000000
mean     310.595905
std      121.178218
min      100.455501
25%      216.429447
50%      318.110776
75%      424.109079
max      499.858182
Name: debit, dtype: float64


%%file missing.csv
A,B,C
11,,13
21,22,23
31,32,33

Overwriting missing.csv


missing_data = pd.read_csv("missing.csv")


missing_data


missing_data.B

0     NaN
1    22.0
2    32.0
Name: B, dtype: float64


missing_data.B.sum() # this will internally ignore rows with NaN (or missing values)

54.0


missing_data.describe()


s1 = pd.Series([420,320,120,220])

s1

0    420
1    320
2    120
3    220
dtype: int64


s1[0]

420


s1[3]

220


[i*i for i in s1]

[176400, 102400, 14400, 48400]


s1.sum()

1080


s1.head()

0    420
1    320
2    120
3    220
dtype: int64


s1.tail()

0    420
1    320
2    120
3    220
dtype: int64


stocks = pd.Series([420,320,120,220], 
                   index=["APPLE","AT&T","IBM","NIKE"])


stocks # this series has labels to rows

APPLE    420
AT&T     320
IBM      120
NIKE     220
dtype: int64


stocks['APPLE']

420


stocks[0]

420


[s for s in stocks]

[420, 320, 120, 220]


missing_data


missing_data.A

0    11
1    21
2    31
Name: A, dtype: int64


missing_data.B

0     NaN
1    22.0
2    32.0
Name: B, dtype: float64


missing_data.C

0    13
1    23
2    33
Name: C, dtype: int64


stocksdf = pd.DataFrame({"value": [420.0, 320.5, 120.1, 220.0],
              "high": [423, 322, 125, 227.0],
              "low": [419, 318, 115, 217.4],
              "volume": [100, 200, 50, 1000]},
             index = ["APPLE","AT&T","IBM","NIKE"])


stocksdf


stocksdf.value

APPLE    420.0
AT&T     320.5
IBM      120.1
NIKE     220.0
Name: value, dtype: float64


stocksdf.high

APPLE    423.0
AT&T     322.0
IBM      125.0
NIKE     227.0
Name: high, dtype: float64


stocksdf.volume

APPLE     100
AT&T      200
IBM        50
NIKE     1000
Name: volume, dtype: int64


stocksdf['APPLE']

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/usr/local/default/lib/python3.10/site-packages/pandas/core/indexes/base.py:3803, in Index.get_loc(self, key, method, tolerance)
   3802 try:
-> 3803     return self._engine.get_loc(casted_key)
   3804 except KeyError as err:

File ~/usr/local/default/lib/python3.10/site-packages/pandas/_libs/index.pyx:138, in pandas._libs.index.IndexEngine.get_loc()

File ~/usr/local/default/lib/python3.10/site-packages/pandas/_libs/index.pyx:165, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:5745, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:5753, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'APPLE'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[92], line 1
----> 1 stocksdf['APPLE']

File ~/usr/local/default/lib/python3.10/site-packages/pandas/core/frame.py:3805, in DataFrame.__getitem__(self, key)
   3803 if self.columns.nlevels > 1:
   3804     return self._getitem_multilevel(key)
-> 3805 indexer = self.columns.get_loc(key)
   3806 if is_integer(indexer):
   3807     indexer = [indexer]

File ~/usr/local/default/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key, method, tolerance)
   3803     return self._engine.get_loc(casted_key)
   3804 except KeyError as err:
-> 3805     raise KeyError(key) from err
   3806 except TypeError:
   3807     # If we have a listlike key, _check_indexing_error will raise
   3808     #  InvalidIndexError. Otherwise we fall through and re-raise
   3809     #  the TypeError.
   3810     self._check_indexing_error(key)

KeyError: 'APPLE'


stocksdf.loc['APPLE'] # row with label 'APPLE'

value     420.0
high      423.0
low       419.0
volume    100.0
Name: APPLE, dtype: float64


stocksdf


stocksdf.iloc[0]

value     420.0
high      423.0
low       419.0
volume    100.0
Name: APPLE, dtype: float64


stocksdf.loc[['APPLE','IBM']]


wallet


wallet = pd.read_csv("wallet.csv", index_col=0) # you can specify index column, it can be a number or a column name


wallet


stocksdf.to_csv("stocks.csv")


pd.read_csv("stocks.csv") # by defaulf label is not taken from file, it is take as row number


pd.read_csv("stocks.csv", index_col=0)


!cat stocks.csv

,value,high,low,volume
APPLE,420.0,423.0,419.0,100
AT&T,320.5,322.0,318.0,200
IBM,120.1,125.0,115.0,50
NIKE,220.0,227.0,217.4,1000


%%file stocks_ticker.csv
value,ticker,high,low,volume
420.0,APPLE,423.0,419.0,100
320.5,AT&T,322.0,318.0,200
120.1,IBM,125.0,115.0,50
220.0,NIKE,227.0,217.4,1000

Writing stocks_ticker.csv


pd.read_csv("stocks_ticker.csv")


pd.read_csv("stocks_ticker.csv", index_col="ticker")


wallet


wallet.debit > 300

s1

0    420
1    320
2    120
3    220
dtype: int64


s1 > 200 # this is like a vector operation

0     True
1     True
2    False
3     True
dtype: bool


[1, 1, 1] + [1, 1, 1] # concatenates

[1, 1, 1, 1, 1, 1]


pd.Series([1, 1, 1]) + pd.Series([1, 1, 1]) # vector addition

0    2
1    2
2    2
dtype: int64


s1 + s1

0    840
1    640
2    240
3    440
dtype: int64


s1 - s1

0    0
1    0
2    0
3    0
dtype: int64


s1 * 100

0    42000
1    32000
2    12000
3    22000
dtype: int64


s1 > 300

0     True
1     True
2    False
3    False
dtype: bool


wallet.debit > 300

0      True
1      True
2     False
3     False
4      True
      ...  
95     True
96     True
97     True
98     True
99    False
Name: debit, Length: 100, dtype: bool


greaterthan300 = wallet.debit > 300


greaterthan300.sum() # True -> 1, False = 0

55


wallet.debit[greaterthan300] # this works a filter... it removes rows which has False and keeps rows with has True

0     421.207327
1     328.440080
4     494.128492
7     457.183104
9     443.618884
10    328.175421
11    434.495468
12    329.536003
14    485.297743
16    390.316876
17    316.878675
18    433.822404
20    365.921808
21    329.097372
22    489.143483
23    354.940241
24    498.100496
25    483.315864
28    358.459933
32    345.030436
33    449.248030
34    499.858182
35    441.602143
36    472.941439
38    494.124399
41    425.187191
43    486.033933
46    359.324972
47    310.408610
48    310.058410
50    463.001875
51    331.227023
52    403.610070
55    423.749708
56    319.342876
60    382.519510
62    497.770860
63    355.989050
67    324.786917
69    300.524620
74    303.055421
75    425.625291
77    482.152343
78    415.372894
79    321.763416
81    470.080995
82    411.142701
83    451.584407
87    378.820641
92    356.842638
94    431.185537
95    388.671213
96    467.554562
97    320.789434
98    442.096469
Name: debit, dtype: float64

s1

0    420
1    320
2    120
3    220
dtype: int64


s1[s1 > 200]

0    420
1    320
3    220
dtype: int64


s1[s1 <= 200]

2    120
dtype: int64


wallet[wallet.debit>300]


wallet[wallet.category=="Books"]


def compute_expenses(dataframe, category):
    return dataframe[dataframe.category == category]['debit'].sum()


compute_expenses(wallet, "Books")

4929.750393283798


compute_expenses(wallet, "Food")

8281.189172581233


compute_expenses(wallet, "Travel")

6052.931876440963


wallet = pd.read_csv("wallet.csv")


wallet


del wallet['Unnamed: 0']


wallet


stocksdf


total_asset = stocksdf['value'] * stocksdf['volume']


total_asset

APPLE     42000.0
AT&T      64100.0
IBM        6005.0
NIKE     220000.0
dtype: float64


stocksdf['total_asset'] = total_asset


stocksdf


s1  = pd.Series([2, 3, 4, 5] , index=["a","b","c","d"])

s1

a    2
b    3
c    4
d    5
dtype: int64


s2 = pd.Series([1, 2, 3, 4, 5], index=["e","d","c","b","a"])

s1

a    2
b    3
c    4
d    5
dtype: int64

s2

e    1
d    2
c    3
b    4
a    5
dtype: int64


s1 + s2

a    7.0
b    7.0
c    7.0
d    7.0
e    NaN
dtype: float64


stocksdf


stocksdf.columns

Index(['value', 'high', 'low', 'volume', 'total_asset'], dtype='object')


reordered_cols = stocksdf[['value',  'total_asset', 'high', 'low', 'volume']]


reordered_cols


stocksdf.rename(columns= dict(zip(['value', 'high', 'low', 'volume', 'total_asset'],
                                 ['Value', 'High', 'Low', 'Volume', 'Total_Asset'])))


stocksdf


df = stocksdf.rename(columns= dict(zip(['value', 'high', 'low', 'volume', 'total_asset'],
                                 ['Value', 'High', 'Low', 'Volume', 'Total_Asset'])))

df


df.rename(columns={"Value":"Hello"})


food = wallet[wallet.category=="Food"]


wallet


food


stocksdf[['value',  'total_asset', 'high', 'low', 'volume']]


stocksdf


wallet = pd.read_csv("wallet.csv", index_col=0)


wallet


wallet.category

0       Music
1        Food
2       Books
3     Utility
4       Books
       ...   
95    Utility
96      Books
97    Utility
98     Travel
99       Food
Name: category, Length: 100, dtype: object


wallet.describe()


wallet.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 4 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   date         100 non-null    object 
 1   category     100 non-null    object 
 2   description  100 non-null    object 
 3   debit        100 non-null    float64
dtypes: float64(1), object(3)
memory usage: 3.9+ KB


wallet.category.str.upper() # this will apply this string method to every row from this column

0       MUSIC
1        FOOD
2       BOOKS
3     UTILITY
4       BOOKS
       ...   
95    UTILITY
96      BOOKS
97    UTILITY
98     TRAVEL
99       FOOD
Name: category, Length: 100, dtype: object


wallet.category.str.lower()

0       music
1        food
2       books
3     utility
4       books
       ...   
95    utility
96      books
97    utility
98     travel
99       food
Name: category, Length: 100, dtype: object


wallet.date

0     2021-03-07 14:53:28.377359
1     2020-10-08 09:53:28.377359
2     2021-02-23 09:53:28.377359
3     2020-11-01 14:53:28.377359
4     2021-06-05 13:53:28.377359
                 ...            
95    2021-07-19 13:53:28.377359
96    2021-01-12 19:53:28.377359
97    2021-03-25 11:53:28.377359
98    2021-05-13 15:53:28.377359
99    2020-10-11 16:53:28.377359
Name: date, Length: 100, dtype: object


wallet.debit < 300

0     False
1     False
2      True
3      True
4     False
      ...  
95    False
96    False
97    False
98    False
99     True
Name: debit, Length: 100, dtype: bool


pd.to_datetime(wallet.date)

0    2021-03-07 14:53:28.377359
1    2020-10-08 09:53:28.377359
2    2021-02-23 09:53:28.377359
3    2020-11-01 14:53:28.377359
4    2021-06-05 13:53:28.377359
                ...            
95   2021-07-19 13:53:28.377359
96   2021-01-12 19:53:28.377359
97   2021-03-25 11:53:28.377359
98   2021-05-13 15:53:28.377359
99   2020-10-11 16:53:28.377359
Name: date, Length: 100, dtype: datetime64[ns]


wallet.date

0     2021-03-07 14:53:28.377359
1     2020-10-08 09:53:28.377359
2     2021-02-23 09:53:28.377359
3     2020-11-01 14:53:28.377359
4     2021-06-05 13:53:28.377359
                 ...            
95    2021-07-19 13:53:28.377359
96    2021-01-12 19:53:28.377359
97    2021-03-25 11:53:28.377359
98    2021-05-13 15:53:28.377359
99    2020-10-11 16:53:28.377359
Name: date, Length: 100, dtype: object


import datetime
pd.to_datetime(wallet.date) > datetime.datetime(2020, 12, 31)

0      True
1     False
2      True
3     False
4      True
      ...  
95     True
96     True
97     True
98     True
99    False
Name: date, Length: 100, dtype: bool


wallet['date'] = pd.to_datetime(wallet.date) # this has modified original dataframe


wallet.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 4 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   date         100 non-null    datetime64[ns]
 1   category     100 non-null    object        
 2   description  100 non-null    object        
 3   debit        100 non-null    float64       
dtypes: datetime64[ns](1), float64(1), object(2)
memory usage: 3.9+ KB


wallet[wallet.date < datetime.datetime(2020, 12, 31)]


!cat stocks1.csv

value,ticker,high,low,volume
420.0,APPLE,423.0,419.0,100
320.5,AT&T,322.0,318.0,200
120.1,IBM,125.0,115.0,50
220.0,NIKE,227.0,217.4,1000


%%file stocks2.csv
value,ticker,high,low,volume
$420.0,APPLE,423.0,419.0,100
$320.5,AT&T,322.0,318.0,200
$120.1,IBM,125.0,115.0,50
$220.0,NIKE,227.0,217.4,1000

Writing stocks2.csv


stocks2  = pd.read_csv("stocks2.csv")


stocks2


stocks2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   value   4 non-null      object 
 1   ticker  4 non-null      object 
 2   high    4 non-null      float64
 3   low     4 non-null      float64
 4   volume  4 non-null      int64  
dtypes: float64(2), int64(1), object(2)
memory usage: 288.0+ bytes


stocks2.value* stocks2.volume

0    $420.0$420.0$420.0$420.0$420.0$420.0$420.0$420...
1    $320.5$320.5$320.5$320.5$320.5$320.5$320.5$320...
2    $120.1$120.1$120.1$120.1$120.1$120.1$120.1$120...
3    $220.0$220.0$220.0$220.0$220.0$220.0$220.0$220...
dtype: object


"dfdsf" * 5

'dfdsfdfdsfdfdsfdfdsfdfdsf'


stocks2.value.str.replace("$","")

/tmp/ipykernel_37473/826872926.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  stocks2.value.str.replace("$","")

0    420.0
1    320.5
2    120.1
3    220.0
Name: value, dtype: object


stocks2['value'] = pd.to_numeric(stocks2.value.str.replace("$",""))

/tmp/ipykernel_37473/1359183244.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  stocks2['value'] = pd.to_numeric(stocks2.value.str.replace("$",""))


stocks2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   value   4 non-null      float64
 1   ticker  4 non-null      object 
 2   high    4 non-null      float64
 3   low     4 non-null      float64
 4   volume  4 non-null      int64  
dtypes: float64(3), int64(1), object(1)
memory usage: 288.0+ bytes


stocks2.value* stocks2.volume

0     42000.0
1     64100.0
2      6005.0
3    220000.0
dtype: float64


%%file df.csv
A,B,C,D
a1,34.0,56,(343)
a2,32.0,78,(544)
a3,43.0,79,(545)

Writing df.csv


df = pd.read_csv("df.csv")

df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   A       3 non-null      object 
 1   B       3 non-null      float64
 2   C       3 non-null      int64  
 3   D       3 non-null      object 
dtypes: float64(1), int64(1), object(2)
memory usage: 224.0+ bytes


col = df.D.str.replace("(","-")

/tmp/ipykernel_37473/223839002.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  col = df.D.str.replace("(","-")

col

0    -343)
1    -544)
2    -545)
Name: D, dtype: object


col.str.replace(")","")

/tmp/ipykernel_37473/3983318474.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  col.str.replace(")","")

0    -343
1    -544
2    -545
Name: D, dtype: object


df['D'] = pd.to_numeric(col.str.replace(")",""))

/tmp/ipykernel_37473/3102900507.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  df['D'] = pd.to_numeric(col.str.replace(")",""))

df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   A       3 non-null      object 
 1   B       3 non-null      float64
 2   C       3 non-null      int64  
 3   D       3 non-null      int64  
dtypes: float64(1), int64(2), object(1)
memory usage: 224.0+ bytes


wallet = pd.read_csv("wallet.csv" , index_col=0)


wallet


wallet.groupby("description").sum()

/tmp/ipykernel_37473/4180037204.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  wallet.groupby("description").sum()


wallet.groupby("description")['debit'].max()

description
Amazon           498.100496
Amazon Kindle    497.770860
Auto             494.124399
Electricity      382.519510
Flipcart         494.128492
Hotel            483.315864
Metro            441.602143
Netflix          354.940241
Phone            499.858182
Swiggy           328.440080
Taxi             485.297743
Zomato           489.143483
spotify          415.372894
Name: debit, dtype: float64


def find_max_in_shop(data, shopname):
    maxvalues = data.groupby("description")['debit'].max()
    return maxvalues.loc[shopname]


find_max_in_shop(wallet, "Flipcart")

494.1284923793595


max(["one", "two", "three"])

'two'


wallet[wallet.description=="Amazon"]


wallet.groupby(["category", "description"]).sum()

/tmp/ipykernel_37473/4010557707.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  wallet.groupby(["category", "description"]).sum()


df = wallet.groupby(["category", "description"]).sum()

/tmp/ipykernel_37473/807199200.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df = wallet.groupby(["category", "description"]).sum()

df


df.loc[('Books','Amazon')]

debit    1037.442802
Name: (Books, Amazon), dtype: float64

s1

a    2
b    3
c    4
d    5
dtype: int64

s2

e    1
d    2
c    3
b    4
a    5
dtype: int64


s1 + s2

a    7.0
b    7.0
c    7.0
d    7.0
e    NaN
dtype: float64


[1, 1, 1] + [1, 1, 1]

[1, 1, 1, 1, 1, 1]


pd.concat([s1, s2])

a    2
b    3
c    4
d    5
e    1
d    2
c    3
b    4
a    5
dtype: int64


stocks

APPLE    420
AT&T     320
IBM      120
NIKE     220
dtype: int64


stocksdf


labels = ["APPLE","AT&T","IBM","NIKE"]
value = pd.Series([234.5, 221.6, 125.7, 100.5], index=labels)
high = pd.Series([240.32, 222.5, 127.3, 105.0], index=labels)
low = pd.Series([233.0, 220.0, 123.0, 104.0], index=labels)
volume = pd.Series([100, 200, 50, 1000], index=labels)
stock1 = pd.DataFrame({"value":value, "high":high, "low":low, "volume":volume})

stocks2 = pd.DataFrame({
        "value" : [125, 500.0, 300.4, 423.9],
        "low" : [125.0, 490.0, 299.5, 421.1],
        "high" : [130.0, 500.0, 305.0, 425.5],
        "volume" : [123, 50, 100, 80]
        },
        index = ["BELL","XEROX","FORD","TESLA"]
    )


stock1


stocks2


pd.concat([stock1, stocks2])


labels = ["APPLE","AT&T","IBM","NIKE"]
value = pd.Series([234.5, 221.6, 125.7, 100.5], index=labels)
high = pd.Series([240.32, 222.5, 127.3, 105.0], index=labels)
low = pd.Series([233.0, 220.0, 123.0, 104.0], index=labels)
volume = pd.Series([100, 200, 50, 1000], index=labels)
stocks1 = pd.DataFrame({"value":value, "high":high, "low":low, "volume":volume})

stocks2 = pd.DataFrame({
        "value" : [125, 500.0, 300.4, 423.9],
        "volume" : [123, 50, 100, 80],
         "low" : [125.0, 490.0, 299.5, 421.1],
        "high" : [130.0, 500.0, 305.0, 425.5]
        },
        index = ["BELL","XEROX","FORD","TESLA"]
    )


stocks1


stocks2


pd.concat([stocks1, stocks2])


!cat stocks.csv

,value,high,low,volume
APPLE,420.0,423.0,419.0,100
AT&T,320.5,322.0,318.0,200
IBM,120.1,125.0,115.0,50
NIKE,220.0,227.0,217.4,1000


%%file stocks_data1.csv
ticker,value,high
APPLE,420.0,423.0
AT&T,320.5,322.0
IBM,120.1,125.0
NIKE,220.0,227.0

Overwriting stocks_data1.csv


%%file stocks_data2.csv
ticker,low,volume
APPLE,419.0,100
AT&T,318.0,200
IBM,115.0,50
NIKE,217.4,1000

Overwriting stocks_data2.csv


data1 = pd.read_csv("stocks_data1.csv", index_col="ticker")
data2 = pd.read_csv("stocks_data2.csv", index_col="ticker")


data1


data2


data1.join(data2) # just like SQL ..this needs same index on both dataframes


df1 = pd.read_csv("stocks_data1.csv")
df2 = pd.read_csv("stocks_data2.csv")

df1

df2


pd.merge(df1, df2, on="ticker")


wallet.to_csv("data.csv")

Python Virtual Training For Arcesium - Module III - Day 1¶

Pandas - Spreadsheet of python¶

DataFrame and Series¶

Series¶

Selecting and filtering¶

Selecting and Filtering¶

deleting and adding column¶

str operations¶

Str operations on str columns and column type conversion¶

groupby¶

Combining two dataframes¶

	Unnamed: 0	date	category	description	debit
0	0	2021-03-07 14:53:28.377359	Music	Amazon	421.207327
1	1	2020-10-08 09:53:28.377359	Food	Swiggy	328.440080
2	2	2021-02-23 09:53:28.377359	Books	Amazon	244.679437
3	3	2020-11-01 14:53:28.377359	Utility	Phone	222.756318
4	4	2021-06-05 13:53:28.377359	Books	Flipcart	494.128492
...	...	...	...	...	...
95	95	2021-07-19 13:53:28.377359	Utility	Phone	388.671213
96	96	2021-01-12 19:53:28.377359	Books	Flipcart	467.554562
97	97	2021-03-25 11:53:28.377359	Utility	Phone	320.789434
98	98	2021-05-13 15:53:28.377359	Travel	Taxi	442.096469
99	99	2020-10-11 16:53:28.377359	Food	Hotel	100.455501

	Unnamed: 0	debit
count	100.000000	100.000000
mean	49.500000	310.595905
std	29.011492	121.178218
min	0.000000	100.455501
25%	24.750000	216.429447
50%	49.500000	318.110776
75%	74.250000	424.109079
max	99.000000	499.858182

	Stock Name	Sector	LTP	Change	%Chg
0	Infosys	Computers - Software	1448.65	-22.90	-1.56
1	M&M	Auto - Cars & Jeeps	1210.00	-16.70	-1.36
2	Adani Ports	Infrastructure - General	689.00	-8.80	-1.26
3	Tata Motors	Auto - LCVs & HCVs	431.15	-4.70	-1.08
4	Hero Motocorp	Auto - 2 & 3 Wheelers	2415.15	-25.15	-1.03
5	SBI Life Insura	Life & Health Insurance	1085.20	-10.70	-0.98
6	BPCL	Refineries	323.00	-3.05	-0.94
7	HCL Tech	Computers - Software	1106.90	-9.40	-0.84
8	Coal India	Mining & Minerals	222.55	-1.80	-0.80
9	Dr Reddys Labs	Pharmaceuticals	4361.00	-34.55	-0.79
10	Maruti Suzuki	Auto - Cars & Jeeps	8550.00	-51.55	-0.60
11	TATA Cons. Prod	Plantations - Tea & Coffee	700.55	-3.85	-0.55
12	HDFC Life	Life & Health Insurance	487.75	-2.50	-0.51
13	Wipro	Computers - Software	387.25	-1.85	-0.48
14	Eicher Motors	Auto - LCVs & HCVs	3103.30	-12.80	-0.41
15	SBI	Banks - Public Sector	545.40	-1.95	-0.36
16	ITC	Diversified	386.60	-1.40	-0.36
17	Titan Company	Miscellaneous	2367.95	-7.55	-0.32
18	Bajaj Finserv	Finance - Investments	1324.55	-4.30	-0.32
19	Nestle	Food Processing	18430.00	-55.75	-0.30
20	Divis Labs	Pharmaceuticals	2779.50	-8.00	-0.29
21	Tata Steel	Iron & Steel	107.80	-0.30	-0.28
22	TCS	Computers - Software	3322.80	-8.20	-0.25
23	Sun Pharma	Pharmaceuticals	953.30	-2.25	-0.24
24	NTPC	Power - Generation & Distribution	180.35	-0.40	-0.22
25	Bajaj Auto	Auto - 2 & 3 Wheelers	3816.20	-5.70	-0.15
26	Axis Bank	Banks - Private Sector	850.90	-1.00	-0.12
27	UltraTechCement	Cement - Major	7218.00	-8.40	-0.12
28	Bharti Airtel	Telecommunications - Service	773.25	-0.50	-0.06
29	Larsen	Infrastructure - General	2156.75	-1.10	-0.05

	Stock Name	Sector	LTP	Change	%Chg
0	Adani Enterpris	Trading	1921.00	24.80	1.31
1	ICICI Bank	Banks - Private Sector	851.80	9.15	1.09
2	Apollo Hospital	Hospitals & Medical Services	4355.00	37.75	0.87
3	Grasim	Diversified	1606.50	10.60	0.66
4	JSW Steel	Steel - Large	683.45	4.05	0.60
5	ONGC	Oil Drilling And Exploration	156.70	0.90	0.58
6	Cipla	Pharmaceuticals	886.20	5.00	0.57
7	Power Grid Corp	Power - Generation & Distribution	227.80	1.25	0.55
8	Kotak Mahindra	Banks - Private Sector	1707.00	7.70	0.45
9	Britannia	Food Processing	4330.00	19.00	0.44
10	HDFC Bank	Banks - Private Sector	1595.20	6.55	0.41
11	HUL	Personal Care	2475.30	9.85	0.40
12	Reliance	Refineries	2331.05	8.35	0.36
13	Bajaj Finance	Finance - NBFC	5893.65	19.50	0.33
14	HDFC	Finance - Housing	2615.30	6.40	0.25
15	Hindalco	Iron & Steel	406.55	0.90	0.22
16	UPL	Chemicals	712.30	0.65	0.09
17	Asian Paints	Paints & Varnishes	2830.15	-0.05	0.00

	A	B	C
count	3.0	2.000000	3.0
mean	21.0	27.000000	23.0
std	10.0	7.071068	10.0
min	11.0	22.000000	13.0
25%	16.0	24.500000	18.0
50%	21.0	27.000000	23.0
75%	26.0	29.500000	28.0
max	31.0	32.000000	33.0

	value	high	low	volume
APPLE	420.0	423.0	419.0	100
AT&T	320.5	322.0	318.0	200
IBM	120.1	125.0	115.0	50
NIKE	220.0	227.0	217.4	1000

	value	high	low	volume
ticker
APPLE	420.0	423.0	419.0	100
AT&T	320.5	322.0	318.0	200
IBM	120.1	125.0	115.0	50
NIKE	220.0	227.0	217.4	1000

	value	total_asset	high	low	volume
APPLE	420.0	42000.0	423.0	419.0	100
AT&T	320.5	64100.0	322.0	318.0	200
IBM	120.1	6005.0	125.0	115.0	50
NIKE	220.0	220000.0	227.0	217.4	1000

	value	ticker	high	low	volume
0	$420.0	APPLE	423.0	419.0	100
1	$320.5	AT&T	322.0	318.0	200
2	$120.1	IBM	125.0	115.0	50
3	$220.0	NIKE	227.0	217.4	1000

	debit
description
Amazon	2504.690567
Amazon Kindle	1389.052376
Auto	2210.428935
Electricity	2885.064355
Flipcart	2503.255216
Hotel	2752.174732
Metro	1216.463665
Netflix	1546.567562
Phone	4677.202878
Swiggy	1936.495366
Taxi	2626.039276
Zomato	3592.519075
spotify	1219.636541

	col1	col2	col3
0	11	12	13
1	21	22	23
2	31	32	33

	value	high	low	volume
APPLE	234.5	240.32	233.0	100
AT&T	221.6	222.50	220.0	200
IBM	125.7	127.30	123.0	50
NIKE	100.5	105.00	104.0	1000

	value	low	high	volume
BELL	125.0	125.0	130.0	123
XEROX	500.0	490.0	500.0	50
FORD	300.4	299.5	305.0	100
TESLA	423.9	421.1	425.5	80