Pandas CSV到Dataframe（用Base64编码的列）

Question

下面的代码用于使用Pandas将防火墙日志从csv摄取到数据帧中。在

df = pd.read_csv('/Users/alistairgillespie/Documents/Projects/COMP5310/Akamai Data/FINAL/data.csv', dtype = {"_time": str, "city": str,"country": str,"lat": str,"long": str,"region": str,"UA": str,"bytes": str,"cliIP": str,"reqHost": str, "reqMethod": str, "reqPath": str,"reqPort": str,"respCT": str,"respLen": str,"status": str,"referer": str,"date": str,"conn": str,"denyData": str,"denyRules": str,"policy": str,"ruleSet": str,"warnRules": str,"warnData": str,"warnSlrs": str,"warnTags": str})

*请原谅长列的柱子

在dataframe中，我希望迭代每一行，并使用unquote和base64decode函数调用解码“denyData”列字段（如果不是NaN）。我尝试使用以下代码来执行此操作：

^{pr2}$

将产生以下错误：

TypeError: argument of type 'float' is not iterable

将csv中的字节列处理为Pandas数据帧的正确方法是什么？这是清除这些数据的正确方法吗？下面是一个数据示例。在

Answer 1

您可以尝试if-else，因为错误显然意味着无法处理NaNs：

for i, row in df.iterrows():
    print(pd.notnull(row))
    if pd.notnull(row):
        df.loc[i, 'denyData'] = base64.b64decode(parse.unquote(row['denyData']))
    else:
        df.loc[i, 'denyData'] = np.nan