DataFrame.drop( labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise' ) # short forms df.drop(axis=None) # deletes a specified column df.drop(index=None) # deletes a specified
# axis=1 tells Python that we want to apply function on columns instead of rows # To delete the column permanently from original dataframe df, we can use the option inplace=True df.drop(['A', 'B', 'C'], axis=1, inplace=True)...
df.drop(columns='class',inplace=True) 5. Set the first 3 rows as NaN 把前3行都设置为NaN df.iloc[:4]=np.nan 我这里设置多了,注意哦 6. Delete the rows that have NaN 把包含NaN的行都删除掉 df.dropna(inplace=True) 7. Reset the index so it begins with 0 again ...
The problem is twofold. Firstly, the timestamp restarts for every new serial number which results in a significantly negative time difference. Secondly, it is unclear whether the date and time are in one column or two. This script presumes that they are in two separate columns. Row,#,Serial...