Module 3 - Day 1

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Pandas

pandas is called as spreadsheet of python

You can install pandas library using pip module as given below

pip install pandas

How to load csv data into pandas

A csv file is located in internet . Url is “https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv”

csvurl = "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv"
!ls *.csv
csvdata.csv  salary.csv  stocks.csv  tabular1.csv  tabular.csv
pd.read_csv("stocks.csv")
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[8], line 1
----> 1 pd.read_csv("stocks.csv")

NameError: name 'pd' is not defined
import pandas as pd
!cat salary.csv
11111,22222,33333,40000,50000
!cat stocks.csv
TATA,200.0,5.5
INFY,2000.0,-5
RELIANCE,1505.5,50.0
HCL,1200,70.5
pd.read_csv("stocks.csv") # just filename is enough because it is in current working directory
TATA 200.0 5.5
0 INFY 2000.0 -5.0
1 RELIANCE 1505.5 50.0
2 HCL 1200.0 70.5 
data = pd.read_csv("stocks.csv")
data # __repr__ method of data is returning something that looks like a nice html table
TATA 200.0 5.5
0 INFY 2000.0 -5.0
1 RELIANCE 1505.5 50.0
2 HCL 1200.0 70.5 
data_internet = pd.read_csv(csvurl)
data_internet
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

100 rows × 5 columns

type(data)
pandas.core.frame.DataFrame
type(data_internet)
pandas.core.frame.DataFrame
excel_url = "https://github.com/vikipedia/python-trainings/raw/master/online_course/source/module2/wallet.xlsx"
pd.read_excel(excel_url)
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

100 rows × 5 columns

problem

  • try to load data from internet from this website “https://www.moneycontrol.com/markets/indian-indices/” using pd.read_html()
  • examin what is returned result
import openpyxl
moneycontrolurl = "https://www.moneycontrol.com/markets/indian-indices/"
moneycontrol = pd.read_html(moneycontrolurl)
mdata = pd.read_html("https://www.moneycontrol.com/markets/indian-indices/")
type(mdata)
list
len(mdata)
6
mdata[0]
Stock Name Sector LTP Change %Chg
0 No Data No Data No Data No Data No Data 
mdata[1]
Stock Name Sector LTP Change %Chg
0 No Data No Data No Data No Data No Data 
mdata[2]
Stock Name Sector LTP Change %Chg
0 ONGC Oil Drilling And Exploration 330.70 -4.60 -1.37
1 Bharti Airtel Telecommunications - Service 1454.50 -15.15 -1.03
2 Cipla Pharmaceuticals 1562.80 -12.70 -0.81
3 Tata Steel Iron & Steel 152.88 -1.08 -0.70
4 JSW Steel Steel - Large 911.60 -6.15 -0.67
5 M&M Auto - Cars & Jeeps 2750.00 -15.15 -0.55
6 TATA Cons. Prod Plantations - Tea & Coffee 1171.50 -5.90 -0.50
7 Tata Motors Auto - LCVs & HCVs 1082.55 -5.15 -0.47
8 ITC Diversified 499.90 -1.55 -0.31
9 Adani Enterpris Trading 3096.00 -6.55 -0.21
10 Adani Ports Infrastructure - General 1495.65 -0.80 -0.05
11 Power Grid Corp Power - Generation & Distribution 340.20 -0.10 -0.03 
mdata[3]
Stock Name Sector LTP Change %Chg
0 Hero Motocorp Auto - 2 & 3 Wheelers 5340.95 152.05 2.93
1 SBI Life Insura Life & Health Insurance 1715.40 43.85 2.62
2 IndusInd Bank Banks - Private Sector 1381.20 33.60 2.49
3 HDFC Life Life & Health Insurance 701.85 16.15 2.36
4 Bajaj Finserv Finance - Investments 1577.65 26.55 1.71
5 BPCL Refineries 349.60 5.80 1.69
6 UltraTechCement Cement - Major 11506.00 190.65 1.68
7 Axis Bank Banks - Private Sector 1171.50 18.25 1.58
8 Eicher Motors Auto - LCVs & HCVs 4882.15 68.85 1.43
9 Shriram Finance Finance - Leasing & Hire Purchase 3116.95 41.35 1.34
10 Hindalco Iron & Steel 667.00 8.15 1.24
11 Kotak Mahindra Banks - Private Sector 1802.60 21.25 1.19
12 Tech Mahindra Computers - Software 1611.15 16.50 1.03
13 LTIMindtree Computers - Software 5734.80 58.70 1.03
14 Bajaj Finance Finance - NBFC 6684.55 68.20 1.03
15 Wipro Computers - Software 524.00 4.25 0.82
16 Grasim Diversified 2619.00 20.60 0.79
17 Infosys Computers - Software 1878.95 14.15 0.76
18 Reliance Refineries 2999.00 22.20 0.75
19 Bajaj Auto Auto - 2 & 3 Wheelers 9828.85 58.20 0.60
20 Coal India Mining & Minerals 525.00 3.00 0.57
21 TCS Computers - Software 4515.05 25.05 0.56
22 Asian Paints Paints & Varnishes 3093.00 16.70 0.54
23 SBI Banks - Public Sector 818.00 4.30 0.53
24 NTPC Power - Generation & Distribution 405.15 2.05 0.51
25 Maruti Suzuki Auto - Cars & Jeeps 12206.15 56.35 0.46
26 HDFC Bank Banks - Private Sector 1637.95 6.40 0.39
27 Nestle Food Processing 2511.00 7.85 0.31
28 Larsen Infrastructure - General 3566.05 11.00 0.31
29 Divis Labs Pharmaceuticals 4682.90 13.00 0.28
30 HCL Tech Computers - Software 1682.95 4.45 0.27
31 ICICI Bank Banks - Private Sector 1179.00 3.10 0.26
32 Britannia Food Processing 5747.50 15.00 0.26
33 Dr Reddys Labs Pharmaceuticals 6922.35 11.00 0.16
34 Sun Pharma Pharmaceuticals 1749.55 1.75 0.10
35 Titan Company Miscellaneous 3467.30 2.45 0.07
36 Apollo Hospital Hospitals & Medical Services 6728.40 3.50 0.05
37 HUL Personal Care 2742.90 0.35 0.01 
mdata[4]
Stock Name Sector LTP Change %Chg
0 No Data No Data No Data No Data No Data 
mdata[5]
Stock Name Sector LTP Change %Chg
0 No Data No Data No Data No Data No Data 
import requests
def download(url, filename):
    r = requests.get(url)
    with open(filename, "wb") as f:
        f.write(r.content)
download(excel_url, "wallet.xlsx")
pd.read_excel("wallet.xlsx", sheet_name="squrs")
x sqr(x)
0 1 1
1 2 4
2 3 9
3 4 16
4 5 25
5 6 36
6 7 49
7 8 64
8 9 81
9 10 100
10 11 121
11 12 144
12 13 169
13 14 196
14 15 225
15 16 256
16 17 289
17 18 324 
pd.read_excel("wallet.xlsx", sheet_name="wallet")
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

100 rows × 5 columns

data
TATA 200.0 5.5
0 INFY 2000.0 -5.0
1 RELIANCE 1505.5 50.0
2 HCL 1200.0 70.5 
wallet = pd.read_csv(csvurl)
wallet
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

100 rows × 5 columns

wallet.info() # method
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Unnamed: 0   100 non-null    int64  
 1   date         100 non-null    object 
 2   category     100 non-null    object 
 3   description  100 non-null    object 
 4   debit        100 non-null    float64
dtypes: float64(1), int64(1), object(3)
memory usage: 4.0+ KB
wallet.head() # first few rows of data
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 
wallet.tail() # last few rows of data
Unnamed: 0 date category description debit
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

Accesing columns from dataframe

wallet.debit # column can be accessed just like class attribute
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'] # just like dictionary
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
column = wallet['debit']
column
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(column)
pandas.core.series.Series
column.sum()
np.float64(31059.590543177277)
column.std()
np.float64(121.17821796983654)
column.max()
np.float64(499.8581815222449)
column.min()
np.float64(100.45550129902664)
column + 3 # vector calculation
0     424.207327
1     331.440080
2     247.679437
3     225.756318
4     497.128492
         ...    
95    391.671213
96    470.554562
97    323.789434
98    445.096469
99    103.455501
Name: debit, Length: 100, dtype: float64
column
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
column*3
0     1263.621982
1      985.320241
2      734.038311
3      668.268953
4     1482.385477
         ...     
95    1166.013640
96    1402.663686
97     962.368301
98    1326.289408
99     301.366504
Name: debit, Length: 100, dtype: float64
import numpy
column + 5.0
0     426.207327
1     333.440080
2     249.679437
3     227.756318
4     499.128492
         ...    
95    393.671213
96    472.554562
97    325.789434
98    447.096469
99    105.455501
Name: debit, Length: 100, dtype: float64
column.min() + 100
np.float64(200.45550129902665)
column.min()
np.float64(100.45550129902664)
column
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

A series can save a data of single type

wallet.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Unnamed: 0   100 non-null    int64  
 1   date         100 non-null    object 
 2   category     100 non-null    object 
 3   description  100 non-null    object 
 4   debit        100 non-null    float64
dtypes: float64(1), int64(1), object(3)
memory usage: 4.0+ KB
wallet.sum()
Unnamed: 0                                                  4950
date           2021-03-07 14:53:28.3773592020-10-08 09:53:28....
category       MusicFoodBooksUtilityBooksUtilityBooksFoodUtil...
description    AmazonSwiggyAmazonPhoneFlipcartElectricityAmaz...
debit                                               31059.590543
dtype: object
wallet.std()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:85, in disallow.__call__.<locals>._f(*args, **kwargs)
     84 try:
---> 85     return f(*args, **kwargs)
     86 except ValueError as e:
     87     # we want to transform an object array
     88     # ValueError message to the more typical TypeError
     89     # e.g. this is normally a disallowed function on
     90     # object arrays that contain strings

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:147, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
    146 else:
--> 147     result = alt(values, axis=axis, skipna=skipna, **kwds)
    149 return result

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:1013, in nanvar(values, axis, skipna, ddof, mask)
   1007 # xref GH10242
   1008 # Compute variance via two-pass algorithm, which is stable against
   1009 # cancellation errors and relatively accurate for small numbers of
   1010 # observations.
   1011 #
   1012 # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
-> 1013 avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
   1014 if axis is not None:

File /opt/tljh/user/lib/python3.10/site-packages/numpy/_core/_methods.py:52, in _sum(a, axis, dtype, out, keepdims, initial, where)
     50 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
     51          initial=_NoValue, where=True):
---> 52     return umr_sum(a, axis, dtype, out, keepdims, initial, where)

ValueError: could not convert string to float: '2021-03-07 14:53:28.377359'

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

TypeError                                 Traceback (most recent call last)
Cell In[69], line 1
----> 1 wallet.std()

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/frame.py:11748, in DataFrame.std(self, axis, skipna, ddof, numeric_only, **kwargs)
  11739 @doc(make_doc("std", ndim=2))
  11740 def std(
  11741     self,
   (...)
  11746     **kwargs,
  11747 ):
> 11748     result = super().std(axis, skipna, ddof, numeric_only, **kwargs)
  11749     if isinstance(result, Series):
  11750         result = result.__finalize__(self, method="std")

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/generic.py:12358, in NDFrame.std(self, axis, skipna, ddof, numeric_only, **kwargs)
  12350 def std(
  12351     self,
  12352     axis: Axis | None = 0,
   (...)
  12356     **kwargs,
  12357 ) -> Series | float:
> 12358     return self._stat_function_ddof(
  12359         "std", nanops.nanstd, axis, skipna, ddof, numeric_only, **kwargs
  12360     )

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/generic.py:12322, in NDFrame._stat_function_ddof(self, name, func, axis, skipna, ddof, numeric_only, **kwargs)
  12319 elif axis is lib.no_default:
  12320     axis = 0
> 12322 return self._reduce(
  12323     func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
  12324 )

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/frame.py:11562, in DataFrame._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
  11558     df = df.T
  11560 # After possibly _get_data and transposing, we are now in the
  11561 #  simple case where we can use BlockManager.reduce
> 11562 res = df._mgr.reduce(blk_func)
  11563 out = df._constructor_from_mgr(res, axes=res.axes).iloc[0]
  11564 if out_dtype is not None and out.dtype != "boolean":

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/internals/managers.py:1500, in BlockManager.reduce(self, func)
   1498 res_blocks: list[Block] = []
   1499 for blk in self.blocks:
-> 1500     nbs = blk.reduce(func)
   1501     res_blocks.extend(nbs)
   1503 index = Index([None])  # placeholder

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/internals/blocks.py:404, in Block.reduce(self, func)
    398 @final
    399 def reduce(self, func) -> list[Block]:
    400     # We will apply the function and reshape the result into a single-row
    401     #  Block with the same mgr_locs; squeezing will be done at a higher level
    402     assert self.ndim == 2
--> 404     result = func(self.values)
    406     if self.values.ndim == 1:
    407         res_values = result

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/frame.py:11481, in DataFrame._reduce.<locals>.blk_func(values, axis)
  11479         return np.array([result])
  11480 else:
> 11481     return op(values, axis=axis, skipna=skipna, **kwds)

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:147, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
    145         result = alt(values, axis=axis, skipna=skipna, **kwds)
    146 else:
--> 147     result = alt(values, axis=axis, skipna=skipna, **kwds)
    149 return result

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:950, in nanstd(values, axis, skipna, ddof, mask)
    947 orig_dtype = values.dtype
    948 values, mask = _get_values(values, skipna, mask=mask)
--> 950 result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
    951 return _wrap_results(result, orig_dtype)

File /opt/tljh/user/lib/python3.10/site-packages/pandas/core/nanops.py:92, in disallow.__call__.<locals>._f(*args, **kwargs)
     86 except ValueError as e:
     87     # we want to transform an object array
     88     # ValueError message to the more typical TypeError
     89     # e.g. this is normally a disallowed function on
     90     # object arrays that contain strings
     91     if is_object_dtype(args[0]):
---> 92         raise TypeError(e) from e
     93     raise

TypeError: could not convert string to float: '2021-03-07 14:53:28.377359'

Series

s1 = pd.Series([12, 23, 34.5, 234, 34, 76])
s2 = pd.Series([2, 2, 2, 2, 2, 2])
s1
0     12.0
1     23.0
2     34.5
3    234.0
4     34.0
5     76.0
dtype: float64
s1 + s2 # vector operation
0     14.0
1     25.0
2     36.5
3    236.0
4     36.0
5     78.0
dtype: float64
s1 - s2
0     10.0
1     21.0
2     32.5
3    232.0
4     32.0
5     74.0
dtype: float64
s1 * s2
0     24.0
1     46.0
2     69.0
3    468.0
4     68.0
5    152.0
dtype: float64
s1/s2
0      6.00
1     11.50
2     17.25
3    117.00
4     17.00
5     38.00
dtype: float64
a = pd.Series([10, 20, 30, 40, 50], index = ['a','b','c','d','e'])
a # we changed the labels!
a    10
b    20
c    30
d    40
e    50
dtype: int64
A = pd.Series([10, 20, 30, 40, 50]) # labels are takes as row number
A
0    10
1    20
2    30
3    40
4    50
dtype: int64
a
a    10
b    20
c    30
d    40
e    50
dtype: int64
A[0]
np.int64(10)
A[1]
np.int64(20)
a['a']
np.int64(10)
a['e']
np.int64(50)
a.iloc[0] # by row number
np.int64(10)
a.iloc[4] #
np.int64(50)
a['d']
np.int64(40)

DataFrame

!cat stocks.csv
TATA,200.0,5.5
INFY,2000.0,-5
RELIANCE,1505.5,50.0
HCL,1200,70.5
pd.read_csv("stocks.csv")
TATA 200.0 5.5
0 INFY 2000.0 -5.0
1 RELIANCE 1505.5 50.0
2 HCL 1200.0 70.5 
%%file stocks1.csv
ticker,value,gain
TATA,200.0,5.5
INFY,2000.0,-5
RELIANCE,1505.5,50.0
HCL,1200,70.5
Writing stocks1.csv
stocks = pd.read_csv("stocks1.csv")
stocks
ticker value gain
0 TATA 200.0 5.5
1 INFY 2000.0 -5.0
2 RELIANCE 1505.5 50.0
3 HCL 1200.0 70.5 
stocks['ticker']
0        TATA
1        INFY
2    RELIANCE
3         HCL
Name: ticker, dtype: object
stocks.ticker
0        TATA
1        INFY
2    RELIANCE
3         HCL
Name: ticker, dtype: object
stocks_labeled = pd.read_csv("stocks1.csv", index_col=0)
stocks_labeled
value gain
ticker
TATA 200.0 5.5
INFY 2000.0 -5.0
RELIANCE 1505.5 50.0
HCL 1200.0 70.5 
stocks_labeled.loc['TATA'] # to access row from dataframe
value    200.0
gain       5.5
Name: TATA, dtype: float64
stocks_labeled.loc[['TATA','RELIANCE']]
value gain
ticker
TATA 200.0 5.5
RELIANCE 1505.5 50.0 
stocks_labeled.iloc[0]
value    200.0
gain       5.5
Name: TATA, dtype: float64
stocks_labeled.iloc[2]
value    1505.5
gain       50.0
Name: RELIANCE, dtype: float64
stocks_labeled.iloc[[0,2,3]]
value gain
ticker
TATA 200.0 5.5
RELIANCE 1505.5 50.0
HCL 1200.0 70.5

problem - Find total debit amount in dataframe loaded from wallet.csv - What is that value of ‘category’ for 70th row.

wallet.debit.sum()
np.float64(31059.590543177277)
wallet.category.iloc[70] # selct a column first , then row
'Utility'
wallet.iloc[70]
Unnamed: 0                             70
date           2021-08-17 13:53:28.377359
category                          Utility
description                         Phone
debit                          125.229773
Name: 70, dtype: object
wallet.iloc[70]['category'] # select a row first, then column
'Utility'
wallet
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

100 rows × 5 columns

wallet['Unnamed: 0'] # if column has special chars and or space then you will have to
                     # access that column using square brackets and columns names in it
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.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

Filtering

s1 = pd.Series([12, 23, 34.5, 234, 34, 76])
s2 = pd.Series([2, 2, 2, 2, 2, 2])
s1 > 30 # boolean series
0    False
1    False
2     True
3     True
4     True
5     True
dtype: bool
s1[s1>30] # this will select rows for which the condition is true
2     34.5
3    234.0
4     34.0
5     76.0
dtype: float64
s2[s1>30]  # take rows from s2 where corresponding row number of s1 has value greater than 30!
2    2
3    2
4    2
5    2
dtype: int64
wallet
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

100 rows × 5 columns

wallet[wallet.category=='Music']
Unnamed: 0 date category description debit
0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
12 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
23 23 2020-12-11 10:53:28.377359 Music Netflix 354.940241
37 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
49 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
54 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
65 65 2020-10-23 18:53:28.377359 Music Netflix 188.748743
67 67 2021-07-31 14:53:28.377359 Music Netflix 324.786917
76 76 2020-11-17 09:53:28.377359 Music Netflix 197.534600
78 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
79 79 2021-08-17 09:53:28.377359 Music Netflix 321.763416
82 82 2020-09-22 09:53:28.377359 Music spotify 411.142701
84 84 2020-09-21 10:53:28.377359 Music Netflix 158.793646
85 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
89 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
90 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276 
wallet
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

100 rows × 5 columns

music = wallet[wallet.category=='Music']
music
Unnamed: 0 date category description debit
0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
12 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
23 23 2020-12-11 10:53:28.377359 Music Netflix 354.940241
37 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
49 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
54 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
65 65 2020-10-23 18:53:28.377359 Music Netflix 188.748743
67 67 2021-07-31 14:53:28.377359 Music Netflix 324.786917
76 76 2020-11-17 09:53:28.377359 Music Netflix 197.534600
78 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
79 79 2021-08-17 09:53:28.377359 Music Netflix 321.763416
82 82 2020-09-22 09:53:28.377359 Music spotify 411.142701
84 84 2020-09-21 10:53:28.377359 Music Netflix 158.793646
85 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
89 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
90 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276 
music.debit.sum() # total expenditure done on Music!
np.float64(4233.45186823271)
music.info()
<class 'pandas.core.frame.DataFrame'>
Index: 16 entries, 0 to 90
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Unnamed: 0   16 non-null     int64  
 1   date         16 non-null     object 
 2   category     16 non-null     object 
 3   description  16 non-null     object 
 4   debit        16 non-null     float64
dtypes: float64(1), int64(1), object(3)
memory usage: 768.0+ bytes
music
Unnamed: 0 date category description debit
0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
12 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
23 23 2020-12-11 10:53:28.377359 Music Netflix 354.940241
37 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
49 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
54 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
65 65 2020-10-23 18:53:28.377359 Music Netflix 188.748743
67 67 2021-07-31 14:53:28.377359 Music Netflix 324.786917
76 76 2020-11-17 09:53:28.377359 Music Netflix 197.534600
78 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
79 79 2021-08-17 09:53:28.377359 Music Netflix 321.763416
82 82 2020-09-22 09:53:28.377359 Music spotify 411.142701
84 84 2020-09-21 10:53:28.377359 Music Netflix 158.793646
85 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
89 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
90 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276 
music.reset_index() # it will set index from 0 to row count
index Unnamed: 0 date category description debit
0 0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
1 12 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
2 23 23 2020-12-11 10:53:28.377359 Music Netflix 354.940241
3 37 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
4 49 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
5 54 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
6 65 65 2020-10-23 18:53:28.377359 Music Netflix 188.748743
7 67 67 2021-07-31 14:53:28.377359 Music Netflix 324.786917
8 76 76 2020-11-17 09:53:28.377359 Music Netflix 197.534600
9 78 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
10 79 79 2021-08-17 09:53:28.377359 Music Netflix 321.763416
11 82 82 2020-09-22 09:53:28.377359 Music spotify 411.142701
12 84 84 2020-09-21 10:53:28.377359 Music Netflix 158.793646
13 85 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
14 89 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
15 90 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276 
stocks_labeled
value gain
ticker
TATA 200.0 5.5
INFY 2000.0 -5.0
RELIANCE 1505.5 50.0
HCL 1200.0 70.5 
stocks_labeled[stocks_labeled.gain>0]
value gain
ticker
TATA 200.0 5.5
RELIANCE 1505.5 50.0
HCL 1200.0 70.5 
mdash = music.reset_index()
mdash['index']
0      0
1     12
2     23
3     37
4     49
5     54
6     65
7     67
8     76
9     78
10    79
11    82
12    84
13    85
14    89
15    90
Name: index, dtype: int64
mdash.index # there is a conflict in name of variable of dataframe and columnname!
RangeIndex(start=0, stop=16, step=1)
music.reset_index(drop=True)
Unnamed: 0 date category description debit
0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
1 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
2 23 2020-12-11 10:53:28.377359 Music Netflix 354.940241
3 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
4 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
5 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
6 65 2020-10-23 18:53:28.377359 Music Netflix 188.748743
7 67 2021-07-31 14:53:28.377359 Music Netflix 324.786917
8 76 2020-11-17 09:53:28.377359 Music Netflix 197.534600
9 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
10 79 2021-08-17 09:53:28.377359 Music Netflix 321.763416
11 82 2020-09-22 09:53:28.377359 Music spotify 411.142701
12 84 2020-09-21 10:53:28.377359 Music Netflix 158.793646
13 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
14 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
15 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276

problem - Find out expenditure done on spotify?

wallet = pd.read_csv(csvurl)
wallet[wallet.description=='spotify']
Unnamed: 0 date category description debit
49 49 2021-03-16 09:53:28.377359 Music spotify 232.303402
54 54 2020-11-16 10:53:28.377359 Music spotify 160.817543
78 78 2020-09-09 16:53:28.377359 Music spotify 415.372894
82 82 2020-09-22 09:53:28.377359 Music spotify 411.142701 
wallet[wallet.description=='spotify']['debit'].sum()
np.float64(1219.6365406108478)
wallet[(wallet.category=='Music') & (wallet.description=='Amazon')]
Unnamed: 0 date category description debit
0 0 2021-03-07 14:53:28.377359 Music Amazon 421.207327
12 12 2021-07-24 14:53:28.377359 Music Amazon 329.536003
37 37 2021-04-18 16:53:28.377359 Music Amazon 266.069078
85 85 2021-01-12 09:53:28.377359 Music Amazon 130.374908
89 89 2021-04-12 14:53:28.377359 Music Amazon 218.487173
90 90 2020-12-01 14:53:28.377359 Music Amazon 101.573276 
s1[s1>30]
2     34.5
3    234.0
4     34.0
5     76.0
dtype: float64
s1[s1<=30]
0    12.0
1    23.0
dtype: float64
s1[~(s1>30)] # ~ will negate the boolean series
0    12.0
1    23.0
dtype: float64
f = s1 > 30
s1[f]
2     34.5
3    234.0
4     34.0
5     76.0
dtype: float64
s1[~f]
0    12.0
1    23.0
dtype: float64
wallet[~(wallet.description=='Amazon')]
Unnamed: 0 date category description debit
1 1 2020-10-08 09:53:28.377359 Food Swiggy 328.440080
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
5 5 2021-07-28 19:53:28.377359 Utility Electricity 219.941711
6 6 2021-04-16 11:53:28.377359 Books Amazon Kindle 270.322595
... ... ... ... ... ...
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

91 rows × 5 columns

groupby

wallet.groupby('category').sum(numeric_only=True)
Unnamed: 0 debit
category
Books 604 4929.750393
Food 1376 8281.189173
Music 970 4233.451868
Travel 721 6052.931876
Utility 1279 7562.267233 
wallet.groupby('description').sum(numeric_only=True)
Unnamed: 0 debit
description
Amazon 411 2504.690567
Amazon Kindle 171 1389.052376
Auto 121 2210.428935
Electricity 536 2885.064355
Flipcart 335 2503.255216
Hotel 565 2752.174732
Metro 139 1216.463665
Netflix 394 1546.567562
Phone 743 4677.202878
Swiggy 386 1936.495366
Taxi 461 2626.039276
Zomato 425 3592.519075
spotify 263 1219.636541 
wallet.groupby(['category', 'description']).sum(numeric_only=True)
Unnamed: 0 debit
category description
Books Amazon 98 1037.442802
Amazon Kindle 171 1389.052376
Flipcart 335 2503.255216
Food Hotel 565 2752.174732
Swiggy 386 1936.495366
Zomato 425 3592.519075
Music Amazon 313 1467.247766
Netflix 394 1546.567562
spotify 263 1219.636541
Travel Auto 121 2210.428935
Metro 139 1216.463665
Taxi 461 2626.039276
Utility Electricity 536 2885.064355
Phone 743 4677.202878 
wallet.groupby('description').max(numeric_only=True)
Unnamed: 0 debit
description
Amazon 90 498.100496
Amazon Kindle 77 497.770860
Auto 38 494.124399
Electricity 93 382.519510
Flipcart 96 494.128492
Hotel 99 483.315864
Metro 73 441.602143
Netflix 84 354.940241
Phone 97 499.858182
Swiggy 80 328.440080
Taxi 98 485.297743
Zomato 87 489.143483
spotify 82 415.372894 
wallet.groupby('description').min(numeric_only=True)
Unnamed: 0 debit
description
Amazon 0 101.573276
Amazon Kindle 6 138.806578
Auto 9 365.921808
Electricity 5 103.680791
Flipcart 4 109.325909
Hotel 25 100.455501
Metro 10 117.588729
Netflix 23 158.793646
Phone 3 124.221248
Swiggy 1 112.333160
Taxi 14 279.147884
Zomato 7 198.450672
spotify 49 160.817543 
multiindexed = wallet.groupby(['category', 'description']).sum(numeric_only=True)
multiindexed
Unnamed: 0 debit
category description
Books Amazon 98 1037.442802
Amazon Kindle 171 1389.052376
Flipcart 335 2503.255216
Food Hotel 565 2752.174732
Swiggy 386 1936.495366
Zomato 425 3592.519075
Music Amazon 313 1467.247766
Netflix 394 1546.567562
spotify 263 1219.636541
Travel Auto 121 2210.428935
Metro 139 1216.463665
Taxi 461 2626.039276
Utility Electricity 536 2885.064355
Phone 743 4677.202878 
multiindexed.loc['Utility']
Unnamed: 0 debit
description
Electricity 536 2885.064355
Phone 743 4677.202878 
multiindexed.loc['Travel']
Unnamed: 0 debit
description
Auto 121 2210.428935
Metro 139 1216.463665
Taxi 461 2626.039276 
multiindexed.loc['Music']
Unnamed: 0 debit
description
Amazon 313 1467.247766
Netflix 394 1546.567562
spotify 263 1219.636541 
multiindexed['debit'].loc['Music'].loc['Netflix']
np.float64(1546.5675619264123)
%%file weeklydata.csv
symbol,day,price
IBM,Monday,111.71436961893693
IBM,Tuesday,141.21220022208635
IBM,Wednesday,112.40571010053796
IBM,Thursday,137.54133351926248
IBM,Friday,140.25154281801224
MICROSOFT,Monday,235.0403622499107
MICROSOFT,Tuesday,225.0206535036475
MICROSOFT,Wednesday,216.10342426936444
MICROSOFT,Thursday,200.38038844494193
MICROSOFT,Friday,235.80850482793264
APPLE,Monday,321.49182055844256
APPLE,Tuesday,340.63612771662815
APPLE,Wednesday,303.9065277507285
APPLE,Thursday,338.1350605764038
APPLE,Friday,318.3912296144338
Writing weeklydata.csv
pd.read_csv("weeklydata.csv")
symbol day price
0 IBM Monday 111.714370
1 IBM Tuesday 141.212200
2 IBM Wednesday 112.405710
3 IBM Thursday 137.541334
4 IBM Friday 140.251543
5 MICROSOFT Monday 235.040362
6 MICROSOFT Tuesday 225.020654
7 MICROSOFT Wednesday 216.103424
8 MICROSOFT Thursday 200.380388
9 MICROSOFT Friday 235.808505
10 APPLE Monday 321.491821
11 APPLE Tuesday 340.636128
12 APPLE Wednesday 303.906528
13 APPLE Thursday 338.135061
14 APPLE Friday 318.391230

problem - Write a function weekly_average to find weekly average from given index data for given ticker

>>> weekly_average(dataframe, "IBM")
def weekly_average(dataframe, ticker):
    return dataframe.price[dataframe.symbol==ticker].mean()
    
df = pd.read_csv("weeklydata.csv")
weekly_average(df, 'IBM')
np.float64(128.62503125576717)
df.groupby('symbol').mean(numeric_only=True)['price'].loc['IBM']
np.float64(128.62503125576717)

Combining data

%%file stocks1.csv
symbol,value,high,low
APPLE,421,422.0,420.4
AT&T,328,328.0,335.0
IBM,123,124.5,120.0
NIKE,234,235.5,230.0
Overwriting stocks1.csv
%%file stocks2.csv
symbol,value,high,low
AGILENT,521,522.0,520.4
XEROX,428,428.0,435.0
TESLA,623,624.5,620.0
Writing stocks2.csv
df1 = pd.read_csv("stocks1.csv")
df2 = pd.read_csv("stocks2.csv")
df1
symbol value high low
0 APPLE 421 422.0 420.4
1 AT&T 328 328.0 335.0
2 IBM 123 124.5 120.0
3 NIKE 234 235.5 230.0 
df2
symbol value high low
0 AGILENT 521 522.0 520.4
1 XEROX 428 428.0 435.0
2 TESLA 623 624.5 620.0 
pd.concat([df1, df2])
symbol value high low
0 APPLE 421 422.0 420.4
1 AT&T 328 328.0 335.0
2 IBM 123 124.5 120.0
3 NIKE 234 235.5 230.0
0 AGILENT 521 522.0 520.4
1 XEROX 428 428.0 435.0
2 TESLA 623 624.5 620.0 
df3 = pd.concat([df1, df2])
df3
symbol value high low
0 APPLE 421 422.0 420.4
1 AT&T 328 328.0 335.0
2 IBM 123 124.5 120.0
3 NIKE 234 235.5 230.0
0 AGILENT 521 522.0 520.4
1 XEROX 428 428.0 435.0
2 TESLA 623 624.5 620.0 
df4 = df3.set_index('symbol')
df3
symbol value high low
0 APPLE 421 422.0 420.4
1 AT&T 328 328.0 335.0
2 IBM 123 124.5 120.0
3 NIKE 234 235.5 230.0
0 AGILENT 521 522.0 520.4
1 XEROX 428 428.0 435.0
2 TESLA 623 624.5 620.0 
df4
value high low
symbol
APPLE 421 422.0 420.4
AT&T 328 328.0 335.0
IBM 123 124.5 120.0
NIKE 234 235.5 230.0
AGILENT 521 522.0 520.4
XEROX 428 428.0 435.0
TESLA 623 624.5 620.0 
df3.set_index('symbol', inplace=True)
df3
value high low
symbol
APPLE 421 422.0 420.4
AT&T 328 328.0 335.0
IBM 123 124.5 120.0
NIKE 234 235.5 230.0
AGILENT 521 522.0 520.4
XEROX 428 428.0 435.0
TESLA 623 624.5 620.0 
hi_low = df3[['high','low']] # list columns
hi_low
high low
symbol
APPLE 422.0 420.4
AT&T 328.0 335.0
IBM 124.5 120.0
NIKE 235.5 230.0
AGILENT 522.0 520.4
XEROX 428.0 435.0
TESLA 624.5 620.0 
value = df3[['value']]
value
value
symbol
APPLE 421
AT&T 328
IBM 123
NIKE 234
AGILENT 521
XEROX 428
TESLA 623 
hi_low
high low
symbol
APPLE 422.0 420.4
AT&T 328.0 335.0
IBM 124.5 120.0
NIKE 235.5 230.0
AGILENT 522.0 520.4
XEROX 428.0 435.0
TESLA 624.5 620.0 
value
value
symbol
APPLE 421
AT&T 328
IBM 123
NIKE 234
AGILENT 521
XEROX 428
TESLA 623 
hi_low.to_csv("hi_low.csv")
value.to_csv("value.csv")
!cat hi_low.csv
symbol,high,low
APPLE,422.0,420.4
AT&T,328.0,335.0
IBM,124.5,120.0
NIKE,235.5,230.0
AGILENT,522.0,520.4
XEROX,428.0,435.0
TESLA,624.5,620.0
!cat value.csv
symbol,value
APPLE,421
AT&T,328
IBM,123
NIKE,234
AGILENT,521
XEROX,428
TESLA,623
df1 = pd.read_csv("hi_low.csv")
df2 = pd.read_csv("value.csv")
df1
symbol high low
0 APPLE 422.0 420.4
1 AT&T 328.0 335.0
2 IBM 124.5 120.0
3 NIKE 235.5 230.0
4 AGILENT 522.0 520.4
5 XEROX 428.0 435.0
6 TESLA 624.5 620.0 
df2
symbol value
0 APPLE 421
1 AT&T 328
2 IBM 123
3 NIKE 234
4 AGILENT 521
5 XEROX 428
6 TESLA 623 
pd.merge(df1, df2, on='symbol')
symbol high low value
0 APPLE 422.0 420.4 421
1 AT&T 328.0 335.0 328
2 IBM 124.5 120.0 123
3 NIKE 235.5 230.0 234
4 AGILENT 522.0 520.4 521
5 XEROX 428.0 435.0 428
6 TESLA 624.5 620.0 623