# Python | Math operations for Data analysis

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
There are some important math operations that can be performed on a pandas series to simplify data analysis using Python and save a lot of time.

```s=read_csv("stock.csv", squeeze=True)
#reading csv file and making series```

Code #1:

## Python3

 `# import pandas for reading csv file``import` `pandas as pd` `#reading csv file``s ``=` `pd.read_csv(``"stock.csv"``, squeeze ``=` `True``)` `#using count function``print``(s.count())` `#using sum function``print``(s.``sum``())` `#using mean function``print``(s.mean())` `#calculation average``print``(s.``sum``()``/``s.count())` `#using std function``print``(s.std())` `#using min function``print``(s.``min``())` `#using max function``print``(s.``max``())` `#using count function``print``(s.median())` `#using mode function``print``(s.mode())`

Output:

```3012
1006942.0
334.3100929614874
334.3100929614874
173.18720477113115
49.95
782.22
283.315
0    291.21```

Code #2:

## Python3

 `# import pandas for reading csv file``import` `pandas as pd` `#reading csv file``s ``=` `pd.read_csv(``"stock.csv"``, squeeze ``=` `True``)` `#using describe function``print``(s.describe())` `#using count function``print``(s.idxmax())` `#using idxmin function``print``(s.idxmin())` `#count of elements having value 3``print``(s.value_counts().head(``3``))`

Output:

```dtype: float64
count    3012.000000
mean      334.310093
std       173.187205
min        49.950000
25%       218.045000
50%       283.315000
75%       443.000000
max       782.220000
Name: Stock Price, dtype: float64

3011
11
291.21    5
288.47    3
194.80    3
Name: Stock Price, dtype: int64```

Unexpected Outputs and Restrictions:

1. .sum(), .mean(), .mode(), .median() and other such mathematical operations are not applicable on string or any other data type than numeric value.
2. .sum() on a string series would give an unexpected output and return a string by concatenating every string.

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