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I am using Python3.5 and I am working with pandas. I have loaded stock data from yahoo finance and have saved the files to csv. My DataFrames load this data from the csv. This is a copy of the ten rows of the csv file that is my DataFrame
Date Open High Low Close Volume Adj Close 1990-04-12 26.875000 26.875000 26.625 26.625 6100 250.576036 1990-04-16 26.500000 26.750000 26.375 26.750 500 251.752449 1990-04-17 26.750000 26.875000 26.750 26.875 2300 252.928863 1990-04-18 26.875000 26.875000 26.500 26.625 3500 250.576036 1990-04-19 26.500000 26.750000 26.500 26.750 700 251.752449 1990-04-20 26.750000 26.875000 26.750 26.875 2100 252.928863 1990-04-23 26.875000 26.875000 26.750 26.875 700 252.928863 1990-04-24 27.000000 27.000000 26.000 26.000 2400 244.693970 1990-04-25 25.250000 25.250000 24.875 25.125 9300 236.459076 1990-04-26 25.000000 25.250000 24.750 25.000 1200 235.282663 I know that I can use iloc, loc, ix but these values that I index will only give my specific rows and columns and will not perform the operation on every row. For example: Row one of the data in the open column has a value of 26.875 and the row below it has 26.50. The price dropped .375 cents. I want to be able to capture the % of Increase or Decrease from the previous day so to finish this example .375 divided by 26.875 = 1.4% decrease from one day to the next. I want to be able to run this calculation on every row so I know how much it has increased or decreased from the previous day. The index functions I have tried but they are absolute, and I don't want to use a loop. Is there a way I can do this with the ix, iloc, loc or another function?
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subtract() function is used for finding the subtraction of dataframe and other, element-wise. This function is essentially same as doing dataframe – other but with a support to substitute for missing data in one of the inputs.
diff() function. This function calculates the difference between two consecutive DataFrame elements. Parameters: periods: Represents periods to shift for computing difference, Integer type value.
We can remove the last n rows using the drop() method. drop() method gets an inplace argument which takes a boolean value. If inplace attribute is set to True then the dataframe gets updated with the new value of dataframe (dataframe with last n rows removed).
you can use pct_change() or/and diff() methods
Demo:
In [138]: df.Close.pct_change() * 100 Out[138]: 0 NaN 1 0.469484 2 0.467290 3 -0.930233 4 0.469484 5 0.467290 6 0.000000 7 -3.255814 8 -3.365385 9 -0.497512 Name: Close, dtype: float64 In [139]: df.Close.diff() Out[139]: 0 NaN 1 0.125 2 0.125 3 -0.250 4 0.125 5 0.125 6 0.000 7 -0.875 8 -0.875 9 -0.125 Name: Close, dtype: float64 MaxU solutions suits in your case. If you want to perform more complex computations based on your previous rows you should use shift
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