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 seires
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:
# 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()) #calculatin 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:
# 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:
- .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.
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