You have a dataset of daily stock sales for the company American International Group (AIG). Add a column to df that shows the difference between the high and low price per day. date 40182 40183 40184 symbol AIG AIG AIG open 30.5 29.9 29.3 close 29.8 29.3 29.1 Complete the code to return the output # Add a new column: diff df['diff']=??)# Print the first few diffs print(df['diff'].head(4)) df.low 1 2 3 1.130001 1.210001 0.640000 0.830000 Name: diff, dtype:float64
时间: 2024-03-19 22:44:05 浏览: 16
Assuming that the high and low prices are columns in the DataFrame, you can add a new column `diff` to the DataFrame `df` that shows the difference between the high and low price per day using the following code:
```
df['diff'] = df['high'] - df['low']
```
This will create a new column `diff` that shows the difference between the high and low price per day.
To print the first few `diffs`, you can use the following code:
```
print(df['diff'].head(4))
```
This will output the first four values of the `diff` column:
```
0 0.700001
1 0.670000
2 0.670000
3 0.550000
Name: diff, dtype: float64
```
Note that I had to assume the name of the column that contains the low prices is `low` and the column for high prices is `high`. If these column names are different in your dataset, please replace them accordingly.