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I just started studying pandas and have questions. Firstly, I'd like to ask this.
I have dataframe and it's like below.
Date Open High Low Close
2015-11-02 711.059998 721.619995 705.849976 721.109985
2015-11-03 718.859985 724.650024 714.719971 722.159973
2015-11-04 722.000000 733.099976 721.900024 728.109985
2015-11-05 729.469971 739.479980 729.469971 731.250000
2015-11-06 731.500000 735.409973 727.010010 733.760010
I know
df["Close"].pct_change()
make the percent change from Close to Close.
But, I want to add a new column, "CloseToOpen" which is a percent change of "yesterday Close to today Open".
So, it is "Open(Day 0) / Close(Day -1) -1". Of course, the first row should be "NaN" or Zero because there's no "previous day's Close".
How can I make this with python pandas code??
Thanks guys!
This is what I want.
Date Open High Low Close CloseToOpen
2015-11-02 711.059998 721.619995 705.849976 721.109985 0.000000
2015-11-03 718.859985 724.650024 714.719971 722.159973 -0.003120
2015-11-04 722.000000 733.099976 721.900024 728.109985 -0.000222
2015-11-05 729.469971 739.479980 729.469971 731.250000 0.001868
2015-11-06 731.500000 735.409973 727.010010 733.760010 0.000342
745
Pandas dataframe.pct_change() function calculates the percentage change between the current and a prior element. This function by default calculates the percentage change from the immediately previous row. Note : This function is mostly useful in the time-series data. Parameters : periods : Periods to shift for forming percent change.
Pandas’ pct_change () function will compute percent change for each value in a column when compared to the previous element in the column by default. Another way to think is Computes the percentage change from the immediately previous row
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. Pandas dataframe.pct_change () function calculates the percentage change between the current and a prior element.
The first row contains NaN values, as there is no previous row from which we can calculate the change. Example #2: Use pct_change () function to find the percentage change in the data which is also having NaN values. The first row contains NaN values, as there is no previous row from which we can calculate the change.
Also you can use standard operands to achieve what you wanted:
df['CloseToOpen'] = (df['Open'] / df['Close'].shift(1) - 1).fillna(0)
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