# 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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