给定索引值的1列中的Pandas DataFrame替换值

Question

This seems like something simple. But I've tried several different things and read probably a dozen StackOverflow questions that seemed related.

I have market data for crypto currnecies (BitCoin and others). The Order Book is a list of all the 'asks' and 'bids' for a given currency. I am wanting to store the data in a Pandas DataFrame. (I'm not stuck on using DFs just thought it might provide easier plotting down the road.)

So the setup right now is a DataFrame where the Rate (price) is the Index and it is a float. The quantity of coins available at that price is in a column named Amt.

When I get an update to the Amount at a given Rate I want to simply replace that value. Of all the possible solutions I found this option to be the most intuitive to me:

df.Amt[df.Rate == rate] = new_amt

While I don't get any errors from this, when I check to see if the Amt changes I find that it has not. I assume this is one of those copy/slice issues vs working on the DataFrame itself.

Example:

df.head()
Out[77]: 
       Rate        Amt
0  0.018021   0.319033
1  0.018009  29.994000
2  0.017999  28.121000
3  0.018042   2.233781
4  0.018055  13.433394

df.Amt[df.Rate == 0.018021] = 20

df.head()
Out[79]: 
       Rate        Amt
0  0.018021   0.319033
1  0.018009  29.994000
2  0.017999  28.121000
3  0.018042   2.233781
4  0.018055  13.433394

What am I missing? I've worked with DFs in the past to change whole columns but not generally just 1 row.

Answer 1

Correcting the error and incorporating @JohnE's comment

You were accessing the pd.Series object with df.Amt and slicing it with [df.Rate == 0.018021] . When what you want is to pass the boolean array to loc and assign that way.

df.loc[np.isclose(df.Rate, 0.018021), 'Amt'] = 20

       Rate        Amt
0  0.018021  20.000000
1  0.018009  29.994000
2  0.017999  28.121000
3  0.018042   2.233781
4  0.018055  13.433394