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

Dec 17-23, 2020 Vikrant Patil

These notes are available online at http://notes.pipal.in/2020/arcesium_finop_batch3/module3-day1.html

We will be using jupyter hub from http://lab.pipal.in for this training. Create a notebook with name module3-day1.ipynb for today's session. Before you start shutdown all kernels except today's notebook.

items = ["Here","are", "some","words","with","different","length"]

numbers = [120, 234, 345, 346, 11]

max(numbers)

346

max(items)

'words'

max(items, key=len)

'different'

[len(w) for w in items]

[4, 3, 4, 5, 4, 9, 6]

max(items, key=len)

'different'

volume = ["1M","2M","0.5B","4M","100K"]

def numeric_part(strv):
    return float(strv[:-1]) # everything except last charecter

def suffix(strv):
    return strv[-1]

def convert(strv):
    n  = numeric_part(strv)
    if suffix(strv)=="M":
        return n*1000000 # take till second last!
    elif suffix(strv)=="B":
        return n*1000000000
    elif suffix(strv)=="K":
        return n*1000
    else:
        return int(strv)

def convert_numeric(strvolume):
    numeric_v = []
    for v in strvolume:
        numeric_v.append(convert(v))
    
    return numeric_v
        
max(convert_numeric(volume))

500000000.0

"34B"[:-1]

'34'

max(convert_numeric(volume))

500000000.0

max(volume, key=convert)

'0.5B'

sorted(volume, key=convert)

['100K', '1M', '2M', '4M', '0.5B']

numeric_volume = convert_numeric(volume)

numeric_volume

[1000000.0, 2000000.0, 500000000.0, 4000000.0, 100000.0]

sorted(numeric_volume)

[100000.0, 1000000.0, 2000000.0, 4000000.0, 500000000.0]

sorted(volume, key=convert)

['100K', '1M', '2M', '4M', '0.5B']

List Comprehensions¶

def convert_numeric(strvolume):
    numeric_v = []
    for v in strvolume:
        numeric_v.append(convert(v))
    
    return numeric_v

def convert_numeric(strvolume):
    return [convert(v) for v in strvolume]

convert_numeric(volume)

[1000000.0, 2000000.0, 500000000.0, 4000000.0, 100000.0]

[x*x for x in range(5)]

[0, 1, 4, 9, 16]

items

['Here', 'are', 'some', 'words', 'with', 'different', 'length']

{word:len(word) for word in items}

{'Here': 4,
 'are': 3,
 'some': 4,
 'words': 5,
 'with': 4,
 'different': 9,
 'length': 6}

lengths = {}
for w in items:
    lengths[w] = len(w)

lengths

{'Here': 4,
 'are': 3,
 'some': 4,
 'words': 5,
 'with': 4,
 'different': 9,
 'length': 6}

Spreadsheet of python, pandas¶

import pandas

!python3 -m pip install --user pandas

Requirement already satisfied: pandas in /home/vikrant/anaconda3/lib/python3.8/site-packages (1.0.5)
Requirement already satisfied: numpy>=1.13.3 in /home/vikrant/anaconda3/lib/python3.8/site-packages (from pandas) (1.18.5)
Requirement already satisfied: pytz>=2017.2 in /home/vikrant/anaconda3/lib/python3.8/site-packages (from pandas) (2020.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /home/vikrant/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)
Requirement already satisfied: six>=1.5 in /home/vikrant/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)
WARNING: You are using pip version 20.2.3; however, version 20.3.3 is available.
You should consider upgrading via the '/home/vikrant/anaconda3/bin/python3 -m pip install --upgrade pip' command.

import pandas

import pandas as pd

%%file download.py
import sys
import requests

def download(url, filename):
    resp = requests.get(url)
    with open(filename, "wb") as f:
        f.write(resp.content)
        
if __name__ == "__main__":
    url = sys.argv[1]
    filename = sys.argv[2]
    download(url, filename)

Overwriting download.py

!python3 download.py "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv" wallet.csv

!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

pd.read_csv("wallet.csv")

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

type(wallet)

pandas.core.frame.DataFrame

wallet

wallet.head()

wallet.tail()

wallet.describe()

pd.read_csv("https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv")

pd.read_excel("https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.xlsx")

!python3 -m pip install --user Xlrd

Requirement already satisfied: Xlrd in /home/vikrant/anaconda3/lib/python3.8/site-packages (1.2.0)
WARNING: You are using pip version 20.2.3; however, version 20.3.3 is available.
You should consider upgrading via the '/home/vikrant/anaconda3/bin/python3 -m pip install --upgrade pip' command.

problems

Load one excel file from your use case. using pandas, see help and load particular sheet from your excel file.
Try to load data from url "https://www.moneycontrol.com/markets/indian-indices/" using pd.read_html

!python3 -m pip install --user lxml

Requirement already satisfied: lxml in /home/vikrant/anaconda3/lib/python3.8/site-packages (4.5.2)
WARNING: You are using pip version 20.2.3; however, version 20.3.3 is available.
You should consider upgrading via the '/home/vikrant/anaconda3/bin/python3 -m pip install --upgrade pip' command.

import pandas as pd
help(pd.read_excel)

Help on function read_excel in module pandas.io.excel._base:

read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds)
    Read an Excel file into a pandas DataFrame.
    
    Supports `xls`, `xlsx`, `xlsm`, `xlsb`, and `odf` file extensions
    read from a local filesystem or URL. Supports an option to read
    a single sheet or a list of sheets.
    
    Parameters
    ----------
    io : str, bytes, ExcelFile, xlrd.Book, 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, and file. For file URLs, a host is
        expected. A local file could be: ``file://localhost/path/to/table.xlsx``.
    
        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 handler (e.g. via builtin ``open`` function)
        or ``StringIO``.
    sheet_name : str, int, list, or None, default 0
        Strings are used for sheet names. Integers are used in zero-indexed
        sheet positions. Lists of strings/integers are used to request
        multiple sheets. Specify None to get all sheets.
    
        Available cases:
    
        * Defaults to ``0``: 1st sheet as a `DataFrame`
        * ``1``: 2nd sheet as a `DataFrame`
        * ``"Sheet1"``: Load sheet with name "Sheet1"
        * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
          as a dict of `DataFrame`
        * None: All sheets.
    
    header : int, list of int, default 0
        Row (0-indexed) to use for the column labels of the parsed
        DataFrame. If a list of integers is passed those row positions will
        be combined into a ``MultiIndex``. Use None if there is no header.
    names : array-like, default None
        List of column names to use. If file contains no header row,
        then you should explicitly pass header=None.
    index_col : int, list of int, default None
        Column (0-indexed) to use as the row labels of the DataFrame.
        Pass None if there is no such column.  If a list is passed,
        those columns will be combined into a ``MultiIndex``.  If a
        subset of data is selected with ``usecols``, index_col
        is based on the subset.
    usecols : int, str, list-like, or callable default None
        * If None, then parse all columns.
        * If str, then indicates comma separated list of Excel column letters
          and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
          both sides.
        * If list of int, then indicates list of column numbers to be parsed.
        * If list of string, then indicates list of column names to be parsed.
    
          .. versionadded:: 0.24.0
    
        * If callable, then evaluate each column name against it and parse the
          column if the callable returns ``True``.
    
        Returns a subset of the columns according to behavior above.
    
          .. versionadded:: 0.24.0
    
    squeeze : bool, default False
        If the parsed data only contains one column then return a Series.
    dtype : Type name or dict of column -> type, default None
        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
        Use `object` to preserve data as stored in Excel and not interpret dtype.
        If converters are specified, they will be applied INSTEAD
        of dtype conversion.
    engine : str, default None
        If io is not a buffer or path, this must be set to identify io.
        Acceptable values are None, "xlrd", "openpyxl" or "odf".
    converters : dict, default None
        Dict of functions for converting values in certain columns. Keys can
        either be integers or column labels, values are functions that take one
        input argument, the Excel cell content, and return the transformed
        content.
    true_values : list, default None
        Values to consider as True.
    false_values : list, default None
        Values to consider as False.
    skiprows : list-like
        Rows to skip at the beginning (0-indexed).
    nrows : int, default None
        Number of rows to parse.
    
        .. versionadded:: 0.23.0
    
    na_values : scalar, str, list-like, or dict, default None
        Additional strings to recognize as NA/NaN. If dict passed, specific
        per-column NA values. By default the following values are interpreted
        as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
        '1.#IND', '1.#QNAN', '<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.
    parse_dates : bool, list-like, or dict, default False
        The behavior is as follows:
    
        * bool. 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 contains an unparseable date, the entire column or
        index will be returned unaltered as an object data type. If you don`t want to
        parse some cells as date just change their type in Excel to "Text".
        For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.
    
        Note: A fast-path exists for iso8601-formatted dates.
    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.
    thousands : str, default None
        Thousands separator for parsing string columns to numeric.  Note that
        this parameter is only necessary for columns stored as TEXT in Excel,
        any numeric columns will automatically be parsed, regardless of display
        format.
    comment : str, default None
        Comments out remainder of line. Pass a character or characters to this
        argument to indicate comments in the input file. Any data between the
        comment string and the end of the current line is ignored.
    skipfooter : int, default 0
        Rows at the end to skip (0-indexed).
    convert_float : bool, default True
        Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
        data will be read in as floats: Excel stores all numbers as floats
        internally.
    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.
    **kwds : optional
            Optional keyword arguments can be passed to ``TextFileReader``.
    
    Returns
    -------
    DataFrame or dict of DataFrames
        DataFrame from the passed in Excel file. See notes in sheet_name
        argument for more information on when a dict of DataFrames is returned.
    
    See Also
    --------
    to_excel : Write DataFrame to an Excel file.
    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
    --------
    The file can be read using the file name as string or an open file object:
    
    >>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
           Name  Value
    0   string1      1
    1   string2      2
    2  #Comment      3
    
    >>> pd.read_excel(open('tmp.xlsx', 'rb'),
    ...               sheet_name='Sheet3')  # doctest: +SKIP
       Unnamed: 0      Name  Value
    0           0   string1      1
    1           1   string2      2
    2           2  #Comment      3
    
    Index and header can be specified via the `index_col` and `header` arguments
    
    >>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP
         0         1      2
    0  NaN      Name  Value
    1  0.0   string1      1
    2  1.0   string2      2
    3  2.0  #Comment      3
    
    Column types are inferred but can be explicitly specified
    
    >>> pd.read_excel('tmp.xlsx', index_col=0,
    ...               dtype={'Name': str, 'Value': float})  # doctest: +SKIP
           Name  Value
    0   string1    1.0
    1   string2    2.0
    2  #Comment    3.0
    
    True, False, and NA values, and thousands separators have defaults,
    but can be explicitly specified, too. Supply the values you would like
    as strings or lists of strings!
    
    >>> pd.read_excel('tmp.xlsx', index_col=0,
    ...               na_values=['string1', 'string2'])  # doctest: +SKIP
           Name  Value
    0       NaN      1
    1       NaN      2
    2  #Comment      3
    
    Comment lines in the excel input file can be skipped using the `comment` kwarg
    
    >>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
          Name  Value
    0  string1    1.0
    1  string2    2.0
    2     None    NaN

r = pd.read_html("https://www.moneycontrol.com/markets/indian-indices/")

type(r)

list

len(r)

4

r[0]

r[1]

r[2]

r[3]

wallet = pd.read_excel("https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.xlsx")

wallet

DataFrame and Series¶

type(wallet)

pandas.core.frame.DataFrame

wallet.columns

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

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.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.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

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['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

type(wallet.debit)

pandas.core.series.Series

wallet

s1 = pd.Series([400, 300, 3244, 500])

s1

0     400
1     300
2    3244
3     500
dtype: int64

[i for i in s1]

[400, 300, 3244, 500]

stocks = pd.Series([400, 300, 3244, 500], index=["APPLE","AT&T","IBM","NIKE"])

stocks # 1D data..equilvalent to column

APPLE     400
AT&T      300
IBM      3244
NIKE      500
dtype: int64

stocks['APPLE']

400

[s for s in stocks]

[400, 300, 3244, 500]

stocks['NIKE']

500

stocks[0]

400

labels = ['APPLE','AT&T','IBM','NIKE']
value = pd.Series([234.0, 221.0, 124.5, 100.4], index=labels)
high = pd.Series([235.0, 225.0 , 125.5, 101.5], index=labels)
low = pd.Series([230.0, 220.0, 120.0, 100.0], index=labels)
volume = pd.Series([100, 235, 125, 300], index=labels)
stocks = pd.DataFrame({
    "value":value,
    "high": high,
    "low": low,
    "volume": volume
    })

stocks

stocks = pd.DataFrame({
    "value":[234.0, 221.0, 124.5, 100.4],
    "high": [235.0, 225.0 , 125.5, 101.5],
    "low": [230.0, 220.0, 120.0, 100.0],
    "volume" : [100, 235, 125, 300]},
    index= labels
)

stocks

stocks1 = pd.DataFrame({
    "value":[234.0, 221.0, 124.5, 100.4],
    "high": [235.0, 225.0 , 125.5, 101.5],
    "low": [230.0, 220.0, 120.0, 100.0],
    "volume" : [100, 235, 125, 300]}
)

stocks1

stocks

stocks['value'] # on a dataframe columns accessible just like dictionary items

APPLE    234.0
AT&T     221.0
IBM      124.5
NIKE     100.4
Name: value, dtype: float64

stocks['value']['APPLE']

234.0

stocks.value

APPLE    234.0
AT&T     221.0
IBM      124.5
NIKE     100.4
Name: value, dtype: float64

stocks.volume

APPLE    100
AT&T     235
IBM      125
NIKE     300
Name: volume, dtype: int64

stocks2 = pd.DataFrame({
    "value":[234.0, 221.0, 124.5, 100.4],
    "high value": [235.0, 225.0 , 125.5, 101.5],
    "low value": [230.0, 220.0, 120.0, 100.0],
    "volume" : [100, 235, 125, 300]},
    index= labels
)

stocks2

stocks2.high value

  File "<ipython-input-49-8e814d9be44a>", line 1
    stocks2.high value
                 ^
SyntaxError: invalid syntax

stocks2['high value']

APPLE    235.0
AT&T     225.0
IBM      125.5
NIKE     101.5
Name: high value, dtype: float64

ways to access data from dataframe¶

stocks

stocks.loc["APPLE"] # complete row of label APPLE

value     234.0
high      235.0
low       230.0
volume    100.0
Name: APPLE, dtype: float64

stocks.loc[['APPLE',"IBM"]]

stocks.loc['APPLE', 'value'] # first item is for row and second item is from column

234.0

stocks.loc[['APPLE','IBM'], 'value']

APPLE    234.0
IBM      124.5
Name: value, dtype: float64

stocks.loc[['APPLE','IBM'], ['value','volume']]

stocks.iloc[0] # gives zeroth row

value     234.0
high      235.0
low       230.0
volume    100.0
Name: APPLE, dtype: float64

stocks.iloc[[0,3]]

stocks.iloc[[0,3],0]

APPLE    234.0
NIKE     100.4
Name: value, dtype: float64

stocks.iloc[[0,3],[0, 3]]

stocks.iloc[:, :] # all the rows and all the columns

stocks.iloc[:2, :] # take first two rows and all the columns

stocks.iloc[:, [0, 3]]

value  = stocks.value

value

APPLE    234.0
AT&T     221.0
IBM      124.5
NIKE     100.4
Name: value, dtype: float64

type(value)

pandas.core.series.Series

value.sum()

679.9

value.cumsum()

APPLE    234.0
AT&T     455.0
IBM      579.5
NIKE     679.9
Name: value, dtype: float64

value.min()

100.4

value.std()

67.35826477377022

value.mean()

169.975

s1

0     400
1     300
2    3244
3     500
dtype: int64

s1.std()

1424.3421873505913

s1.abs()

0     400
1     300
2    3244
3     500
dtype: int64

s1 + 100

0     500
1     400
2    3344
3     600
dtype: int64

s2 = pd.Series([1, 2, 3, 4])

s2

0    1
1    2
2    3
3    4
dtype: int64

s1

0     400
1     300
2    3244
3     500
dtype: int64

s1 + s2

0     401
1     302
2    3247
3     504
dtype: int64

s1 = pd.Series([200, 300, 100], index=['a','b','c'])

s1

a    200
b    300
c    100
dtype: int64

s2 = pd.Series([1, 2, 3], index=['b','a','c'])

s2

b    1
a    2
c    3
dtype: int64

s1 + s2

a    202
b    301
c    103
dtype: int64

stocks

stocks.head()

Selecting /Filtering on some condition¶

stocks

s1

a    200
b    300
c    100
dtype: int64

s1 + 100

a    300
b    400
c    200
dtype: int64

s1 > 100

a     True
b     True
c    False
dtype: bool

s1[s1>100]

a    200
b    300
dtype: int64

stocks

stocks.volume > 200

APPLE    False
AT&T      True
IBM      False
NIKE      True
Name: volume, dtype: bool

stocks[stocks.volume > 200]

wallet

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

food

wallet[wallet.category=="Books"]

food.describe()

wallet[wallet.debit > 450]

wallet[wallet.debit < 450]

wallet[wallet.description=="Amazon"]

wallet[wallet.description=="Taxi"]

class Stock:
    
    def __init__(self, symbol, value, volume):
        self.symbol = symbol
        self.value  = value
        self.volume = volume
        
    def __repr__(self):
        return "Stock({} {} {})".format(self.symbol, self.value, self.volume)

ibm  = Stock("IBM", 120, 100)

%%file stocksdata.csv
"IBM", 120, 100
"IBM1", 121, 101
"IBM2", 122, 102
"IBM3", 123, 103
"IBM4", 124, 104
"IBM5", 125, 105

Writing stocksdata.csv

import csv
def load_as_list(filename):
    with open(filename) as f:
        reader = csv.reader(f)
        print("skipping header", next(reader))
        stocks = []
        for row in reader:
            stocks.append(Stock(row[0], row[1], row[2]))
        return stocks

l = load_as_list("stocksdata.csv")

skipping header ['IBM', ' 120', ' 100']

l[0]

Stock(IBM1  121  101)

def load_as_list(filename):
    with open(filename) as f:
        reader = csv.reader(f)
        print("skipping header", next(reader))
        return [Stock(row[0], row[1], row[2]) for row in reader]

load_as_list("stocksdata.csv")

skipping header ['IBM', ' 120', ' 100']

[Stock(IBM1  121  101),
 Stock(IBM2  122  102),
 Stock(IBM3  123  103),
 Stock(IBM4  124  104),
 Stock(IBM5  125  105)]

def add(x, y):
    return x+y

add(1, 2)

3

args = [1, 2]

add(args[0], args[1])

3

add(*args)

3

def load_as_list(filename):
    with open(filename) as f:
        reader = csv.reader(f)
        print("skipping header", next(reader))
        return [Stock(*row) for row in reader]

load_as_list("stocksdata.csv")

skipping header ['IBM', ' 120', ' 100']

[Stock(IBM1  121  101),
 Stock(IBM2  122  102),
 Stock(IBM3  123  103),
 Stock(IBM4  124  104),
 Stock(IBM5  125  105)]

mat = []
for c in range(5):
    row = []
    for r in range(5):
        if c==r:
            row.append(1)
        else:
            row.append(0)
    mat.append(row)

mat

[[1, 0, 0, 0, 0],
 [0, 1, 0, 0, 0],
 [0, 0, 1, 0, 0],
 [0, 0, 0, 1, 0],
 [0, 0, 0, 0, 1]]

	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

	0	1	2	3	4	5	6	7
0	Asian Paints	2831.30	-0.42	37346	2830.45	2831.85	24.00	22.00
1	Axis Bank	665.00	-1.12	466036	665.00	665.40	108.00	95.00
2	Bajaj Auto	3557.05	0.68	14690	3554.75	3557.80	2.00	27.00
3	Bajaj Finance	4993.00	-1.73	58005	4993.00	4994.95	3.00	30.00
4	Bajaj Finserv	9015.00	-1.70	462082	9018.85	9023.90	2.00	2.00
5	Bharti Airtel	548.00	1.53	915534	548.40	548.65	22.00	45.00
6	Dr Reddys Labs	5408.35	1.33	17159	5407.70	5410.00	2.00	7.00
7	HCL Tech	1031.70	3.76	244597	1031.05	1031.75	76.00	22.00
8	HDFC	2696.25	1.71	85102	2697.20	2698.50	5.00	29.00
9	HDFC Bank	1452.80	1.48	394429	1452.40	1453.10	125.00	46.00
10	HUL	2423.75	1.47	50068	2424.20	2425.40	30.00	4.00
11	ICICI Bank	540.75	-0.12	780610	541.20	541.50	182.00	85.00
12	IndusInd Bank	933.55	-0.59	157458	933.45	933.85	30.00	49.00
13	Infosys	1372.25	4.54	498159	1371.40	1371.85	116.00	11.00
14	ITC	202.00	0.20	2390218	201.85	201.95	774.00	870.00
15	Kotak Mahindra	1950.30	-1.03	40637	1950.50	1951.20	7.00	59.00
16	Larsen	1347.30	-1.87	87734	1347.15	1347.55	10.00	27.00
17	M&M	776.80	0.91	143567	777.60	778.00	107.00	375.00
18	Maruti Suzuki	8240.20	2.81	71990	8237.20	8242.50	15.00	5.00
19	Nestle	18243.30	-0.29	1599	18220.75	18241.35	2.00	5.00
20	NTPC	99.50	-0.60	2334652	99.45	99.55	7758.00	6048.00
21	ONGC	101.80	1.14	2080224	101.80	101.85	2181.00	8506.00
22	Power Grid Corp	202.75	-0.54	254197	202.85	203.00	882.00	166.00
23	Reliance	1910.65	-1.16	169682	1910.45	1910.90	2.00	20.00
24	SBI	281.40	-1.63	3755657	281.25	281.40	825.00	349.00
25	Sun Pharma	622.40	0.33	284708	622.65	622.90	72.00	43.00
26	TCS	3169.60	1.58	194706	3168.90	3169.70	12.00	41.00
27	Tech Mahindra	1049.50	-0.18	115810	1049.10	1049.65	20.00	69.00
28	Titan Company	1552.30	0.16	58136	1551.60	1552.15	15.00	10.00
29	UltraTechCement	5672.20	1.55	10747	5674.75	5678.10	3.00	36.00
30	Company	LTP	%Change	Volume	Buy Price	Sell Price	Buy Qty	Sell Qty

	0	1	2	3	4	5	6
0	SENSEX	49128.12	345.61	0.71	49252.31	49269.02	48956.38
1	S&P BSE Smallcap	18845.03	-63.56	-0.34	19076.89	19085.82	18779.90
2	S&P BSE Midcap	19044.17	-94.55	-0.49	19238.20	19249.01	18954.55
3	S&P BSE SmallCap Select Index	3347.51	-4.42	-0.13	3384.80	3387.03	3330.85
4	S&P BSE MidCap Select Index	8217.59	-28.78	-0.35	8282.73	8285.29	8171.59
5	S&P BSE LargeCap	5547.60	31.70	0.57	5566.59	5567.17	5526.58
6	S&P BSE AllCap	5416.89	17.85	0.33	5445.84	5446.28	5396.34
7	S&P BSE 100	14626.74	73.09	0.50	14681.87	14683.45	14571.05
8	S&P BSE 200	6135.07	25.00	0.41	6162.79	6163.37	6111.46
9	S&P BSE 500	19019.49	65.63	0.35	19118.48	19119.96	18946.88
10	S&P BSE BANKEX	36515.44	-143.33	-0.39	36892.58	36893.81	36352.54
11	S&P BSE Auto	22294.38	296.49	1.35	22120.66	22294.95	21884.64
12	S&P BSE Basic Materials	3683.99	-28.91	-0.78	3722.72	3722.72	3654.72
13	S&P BSE Capital Goods	19631.54	-287.35	-1.44	20043.42	20092.83	19604.07
14	S&P BSE Consumer Discretionary Goods & Services	4682.64	7.55	0.16	4711.13	4720.73	4652.46
15	S&P BSE Consumer Durables	30989.54	-201.20	-0.65	31330.56	31581.91	30985.88
16	S&P BSE Energy	5925.01	-49.00	-0.82	5990.90	5999.47	5915.45
17	S&P BSE Finance	7245.62	3.77	0.05	7302.18	7302.18	7220.82
18	S&P BSE FMCG	12695.68	100.24	0.80	12693.56	12817.12	12657.38
19	S&P BSE Healthcare	22344.27	58.26	0.26	22395.21	22430.73	22203.08
20	S&P BSE India Mfg	529.55	0.75	0.14	531.04	531.56	527.72
21	S&P BSE Industrials	3626.42	-26.20	-0.72	3678.73	3683.33	3617.02
22	S&P BSE IPO	8005.75	-74.51	-0.92	8139.96	8162.48	7998.88
23	S&P BSE IT	26621.47	785.49	3.04	26419.60	26745.41	26302.94
24	S&P BSE Metals	12395.24	-262.69	-2.08	12675.11	12676.88	12354.14
25	S&P BSE Oil and Gas	14714.79	-17.27	-0.12	14775.91	14815.95	14584.81
26	S&P BSE Power	2116.87	-21.88	-1.02	2150.61	2156.78	2115.14
27	S&P BSE PSU	6051.85	-40.68	-0.67	6137.18	6137.61	6015.07
28	S&P BSE Realty	2611.63	27.42	1.06	2609.84	2618.10	2571.25
29	S&P BSE TECk	12146.82	309.68	2.62	12077.45	12199.48	12030.37
30	S&P BSE Telecom	1369.30	7.36	0.54	1368.40	1386.33	1361.98
31	S&P BSE Utilities	1882.50	-5.98	-0.32	1896.16	1904.97	1880.96
32	S&P BSE Dividend Stability Index	492.28	0.99	0.20	494.95	495.02	489.57
33	S&P BSE Enhanced Value Index	294.65	-3.28	-1.10	298.93	298.97	292.67
34	S&P BSE Low Volatility Index	1070.53	4.03	0.38	1072.38	1077.76	1068.38
35	S&P BSE Momentum Index	903.83	5.82	0.65	905.75	908.12	900.30
36	S&P BSE Quality Index	981.42	5.32	0.55	983.05	986.48	979.24
37	S&P BSE SENSEX 50 Index	15117.12	100.06	0.67	15157.41	15161.46	15061.38
38	S&P BSE 150 MidCap	6308.47	-34.44	-0.54	6387.16	6389.84	6282.69
39	S&P BSE 250 SmallCap	2638.39	-5.81	-0.22	2668.07	2670.58	2628.68
40	S&P BSE 250 LargeMid	5786.23	21.96	0.38	5814.22	5814.72	5763.92
41	S&P BSE 400 MidSmall	4721.23	-21.19	-0.45	4778.40	4780.72	4702.93
42	Name	Current Value	Change	% Chg	Open	High	Low

	0	1	2	3	4	5	6
0	NIFTY 50	14436.95	89.70	0.63	14474.05	14479.70	14383.10
1	NIFTY NEXT 50	34315.25	-15.80	-0.05	34566.95	34570.65	34143.60
2	NIFTY Midcap 100	22073.45	-120.65	-0.54	22334.45	22359.00	21965.75
3	NIFTY MIDCAP 50	6339.85	-49.85	-0.78	6420.00	6428.35	6302.30
4	NIFTY 100	14592.05	77.70	0.54	14638.80	14638.95	14535.40
5	NIFTY 500	11967.90	42.30	0.35	12024.65	12024.65	11921.75
6	NIFTY AUTO	9878.30	137.05	1.41	9789.80	9879.05	9693.70
7	NIFTY BANK	31984.45	-99.75	-0.31	32280.30	32288.45	31836.95
8	NIFTY ENERGY	17109.00	-146.15	-0.85	17336.50	17345.30	17052.15
9	NIFTY FMCG	34575.70	298.95	0.87	34597.35	34919.60	34446.35
10	NIFTY INFRA	3821.30	-12.15	-0.32	3844.50	3848.70	3805.50
11	NIFTY IT	26795.30	632.90	2.42	26703.20	26905.65	26528.00
12	NIFTY MEDIA	1732.70	-23.90	-1.36	1773.35	1780.90	1724.65
13	NIFTY METAL	3461.75	-67.45	-1.91	3532.70	3537.40	3451.85
14	NIFTY MNC	16333.70	79.50	0.49	16357.10	16380.75	16253.15
15	NIFTY PHARMA	13383.55	112.00	0.84	13315.45	13400.40	13222.80
16	NIFTY PSE	2955.90	-5.75	-0.19	2978.05	2984.85	2932.60
17	NIFTY PSU BANK	1802.90	-27.05	-1.48	1842.30	1843.75	1791.80
18	NIFTY REALTY	330.85	3.35	1.02	329.85	331.65	325.75
19	NIFTY SERV SECTOR	20526.95	199.05	0.98	20559.50	20585.40	20430.30
20	INDIA VIX	22.31	1.67	8.09	20.64	22.74	20.64
21	SX40	17283.18	-159.32	-0.91	17442.50	17442.50	17283.18
22	NIFTY GROWSECT 15	7137.50	40.05	0.56	7146.05	7163.65	7104.05
23	NIFTY50 VALUE 20	4351.35	48.50	1.13	4310.95	4355.65	4304.25
24	NIFTY DIV OPPS 50	3006.15	18.15	0.61	3021.35	3021.35	2994.85
25	NIFTY MID LIQ 15	5590.90	-55.00	-0.97	5669.35	5679.00	5568.50
26	NIFTY100 QUALITY 30	2705.75	0.20	0.01	2715.95	2719.35	2697.25
27	NIFTY Smallcap 100	7433.75	-9.25	-0.12	7509.10	7525.95	7414.75
28	NIFTY COMMODITIES	3974.60	-24.15	-0.60	4005.55	4006.85	3943.10
29	NIFTY CONSUMPTION	6112.05	39.95	0.66	6119.60	6144.15	6090.30
30	NIFTY FIN SERVICE	15543.75	32.60	0.21	15613.50	15626.85	15493.25
31	NIFTY PVT BANK	17705.40	-50.65	-0.29	17859.10	17863.55	17615.80
32	NIFTY MIDCAP 150	8309.90	-39.20	-0.47	8404.65	8411.60	8273.50
33	NIFTY MIDSML 400	7609.20	-29.60	-0.39	7693.95	7700.00	7579.85
34	NIFTY Smallcap 250	6351.25	-12.80	-0.20	6418.20	6426.20	6332.85
35	NIFTY Smallcap 50	3740.60	8.55	0.23	3768.50	3786.55	3729.05
36	Name	Current Value	Change	% Chg	Open	High	Low

	0	1	2	3	4	5	6
0	SENSEX	49128.12	345.61	0.71	49252.31	49269.02	48956.38
1	S&P BSE Auto	22294.38	296.49	1.35	22120.66	22294.95	21884.64
2	S&P BSE Metals	12395.24	-262.69	-2.08	12675.11	12676.88	12354.14
3	S&P BSE Oil and Gas	14714.79	-17.27	-0.12	14775.91	14815.95	14584.81
4	NIFTY 50	14436.95	89.70	0.63	14474.05	14479.70	14383.10
5	NIFTY NEXT 50	34315.25	-15.80	-0.05	34566.95	34570.65	34143.60
6	NIFTY Midcap 100	22073.45	-120.65	-0.54	22334.45	22359.00	21965.75
7	NIFTY 100	14592.05	77.70	0.54	14638.80	14638.95	14535.40
8	NIFTY BANK	31984.45	-99.75	-0.31	32280.30	32288.45	31836.95
9	NIFTY FMCG	34575.70	298.95	0.87	34597.35	34919.60	34446.35
10	NIFTY INFRA	3821.30	-12.15	-0.32	3844.50	3848.70	3805.50
11	NIFTY IT	26795.30	632.90	2.42	26703.20	26905.65	26528.00
12	NIFTY PHARMA	13383.55	112.00	0.84	13315.45	13400.40	13222.80
13	Name	Current Value	Change	% Chg	Open	High	Low

	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

	value	high	low	volume
APPLE	234.0	235.0	230.0	100
AT&T	221.0	225.0	220.0	235
IBM	124.5	125.5	120.0	125
NIKE	100.4	101.5	100.0	300

	Unnamed: 0	debit
count	27.000000	27.000000
mean	50.962963	306.710710
std	27.784170	119.856277
min	1.000000	100.455501
25%	31.000000	221.239325
50%	51.000000	283.095707
75%	73.000000	430.060379
max	99.000000	489.143483

	Unnamed: 0	date	category	description	debit
14	14	2021-06-09 13:53:28.377359	Travel	Taxi	485.297743
52	52	2021-03-26 09:53:28.377359	Travel	Taxi	403.610070
63	63	2021-05-10 11:53:28.377359	Travel	Taxi	355.989050
68	68	2020-08-26 09:53:28.377359	Travel	Taxi	279.147884
74	74	2021-03-20 11:53:28.377359	Travel	Taxi	303.055421
92	92	2021-01-12 19:53:28.377359	Travel	Taxi	356.842638
98	98	2021-05-13 15:53:28.377359	Travel	Taxi	442.096469