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.
To get the data-set used, click here.
s=read_csv("stock.csv", squeeze=True)
#reading csv file and making series
| Function | Use |
|---|---|
| s.sum() | Returns sum of all values in the series |
| s.mean() |
Returns mean of all values in series. Equals to s.sum()/s.count()
|
| s.std() | Returns standard deviation of all values |
| s.min() or s.max() | Return min and max values from series |
| s.idxmin() or s.idxmax() | Returns index of min or max value in series |
| s.median() | Returns median of all value |
| s.mode() | Returns mode of the series |
| s.value_counts() |
Returns series with frequency of each value
|
| s.describe() |
Returns a series with information like mean, mode, etc depending on dtype of data passed
|
Code #1:
Python3
# import pandas for reading csv fileimport pandas as pd#reading csv files = pd.read_csv("stock.csv", squeeze = True)#using count functionprint(s.count())#using sum functionprint(s.sum())#using mean functionprint(s.mean())#calculation averageprint(s.sum()/s.count())#using std functionprint(s.std())#using min functionprint(s.min())#using max functionprint(s.max())#using count functionprint(s.median())#using mode functionprint(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 fileimport pandas as pd#reading csv files = pd.read_csv("stock.csv", squeeze = True)#using describe functionprint(s.describe())#using count functionprint(s.idxmax())#using idxmin functionprint(s.idxmin())#count of elements having value 3print(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:
- .sum(), .mean(), .mode(), .median() and other such mathematical operations are not applicable on string or any other data type than numeric value.
- .sum() on a string series would give an unexpected output and return a string by concatenating every string.
