pandas，更改 dataframe 中的特定单元格

Question

How to change particular cell in dataframe?

issue:

a new row is adding,instead of modifying the old data

when I try to change it like,

d1.at[0, 'high'] = 10

Answer 1

Assume that your DataFrame ( d1 ) contains something like:

   high  low
1     1    2
2     3    4
3     5    6

Note that the index does not contain 0 .

Then, when you run d1.at[0, 'high'] = 10 , Pandas operates the following way:

• Attempt to find a row with index 0 .
• If the DataFrame in question does not contain such a row then add a row with such index and all columns set initially to NaN .
• In the (just added) row set value in high column to 10 .
• Other columns (in this case only low ) have not been set, so they still have NaN .
• A side effect is that both columns were of int type. But even the initial, temporary setting of any of their elements to NaN coerces these columns to float .

So after the above instruction d1 contains:

   high  low
1   1.0  2.0
2   3.0  4.0
3   5.0  6.0
0  10.0  NaN

And then let's test another scenario:

d1 does contain a row with index 0 :

   high  low
1     1    2
2     3    4
3     5    6
0     7    8

Now if you run d1.at[0, 'high'] = 10 a "wanted" row has been found (no insertion took place) and high column in this row has been set to 10 (other columns in these row keep their initial values).

After that d1 contains:

   high  low
1     1    2
2     3    4
3     5    6
0    10    8

so:

• Pandas updated the column in question,
• both columns keep their initial types.