我在添加dataframe时遇到问题。 我尝试执行此代码
df_all = pd.read_csv('data.csv', error_bad_lines=False, chunksize=1000000)
urls = pd.read_excel('url_june.xlsx')
substr = urls.url.values.tolist()
df_res = pd.DataFrame()
for df in df_all:
for i in substr:
res = df[df['url'].str.contains(i)]
df_res.append(res)
当我尝试保存df_res
时,我得到空数据帧。
df_all
看起来像
ID,"url","used_at","active_seconds"
b20f9412f914ad83b6611d69dbe3b2b4,"mobiguru.ru/phones/apple/comp/32gb/apple_iphone_5s.html",2015-10-01 00:00:25,1
b20f9412f914ad83b6611d69dbe3b2b4,"mobiguru.ru/phones/apple/comp/32gb/apple_iphone_5s.html",2015-10-01 00:00:31,30
f85ce4b2f8787d48edc8612b2ccaca83,"4pda.ru/forum/index.php?showtopic=634566&view=getnewpost",2015-10-01 00:01:49,2
d3b0ef7d85dbb4dbb75e8a5950bad225,"shop.mts.ru/smartfony/mts/smartfon-smart-sprint-4g-sim-lock-white.html?utm_source=admitad&utm_medium=cpa&utm_content=300&utm_campaign=gde_cpa&uid=3",2015-10-01 00:03:19,34
078d388438ebf1d4142808f58fb66c87,"market.yandex.ru/product/12675734/spec?hid=91491&track=char",2015-10-01 00:03:48,2
d3b0ef7d85dbb4dbb75e8a5950bad225,"avito.ru/yoshkar-ola/telefony/mts",2015-10-01 00:04:21,4
d3b0ef7d85dbb4dbb75e8a5950bad225,"shoppingcart.aliexpress.com/order/confirm_order",2015-10-01 00:04:25,1
d3b0ef7d85dbb4dbb75e8a5950bad225,"shoppingcart.aliexpress.com/order/confirm_order",2015-10-01 00:04:26,9
和urls
看起来像
url
shoppingcart.aliexpress.com/order/confirm_order
ozon.ru/?context=order_done&number=
lk.wildberries.ru/basket/orderconfirmed
lamoda.ru/checkout/onepage/success/quick
mvideo.ru/confirmation?_requestid=
eldorado.ru/personal/order.php?step=confirm
当我在循环中打印res
时,它不会为空。但是当我在追加后尝试在循环df_res
中打印时,它返回空数据帧。
我无法找到错误。我该如何解决?
答案 0 :(得分:12)
如果你看the documentation for pd.DataFrame.append
将其他行添加到此框架的末尾,返回新对象。不在此框架中的列将添加为新列。
(强调我的)。
尝试
df_res = df_res.append(res)
顺便提一下,请注意,pandas不是通过连续连接创建DataFrame的有效方法。您可以尝试这样做,而不是:
all_res = []
for df in df_all:
for i in substr:
res = df[df['url'].str.contains(i)]
all_res.append(res)
df_res = pd.concat(all_res)
首先创建所有部分的列表,然后在最后创建一个DataFrame。
答案 1 :(得分:0)
df_res = pd.DataFrame(data = None, columns= df.columns)
all_res = []
d1 = df.ix[index-10:index-1,] #it will take 10 rows before i-th index
all_res.append(d1)
df_res = pd.concat(all_res)