[英]python loop down a column and append output to a dataframe
I am iterating down a column of product codes using df.iterrows(): 我正在使用df.iterrows()迭代一列产品代码:
The codes are then sent to an API and returns various details about my products. 然后将代码发送到API,并返回有关我的产品的各种详细信息。 ie new and used sales prices.
即新的和二手的销售价格。
After each iteration I want to append the data to a dataframe. 每次迭代之后,我想将数据追加到数据框。 I should be seeing 100s of rows of data in my dataframe but instead all I receive is a single row of data for the final product code.
我应该在数据框中看到100行数据,但是我收到的只是最终产品代码的一行数据。
I think I need to create a second nested loop takes my output at each iteration and appends it to a dataframe, but I'm not sure where to begin. 我想我需要创建第二个嵌套循环,每次迭代都将我的输出添加到数据帧中,但是我不确定从哪里开始。 My code is below.
我的代码如下。 Any help would be appreciated.
任何帮助,将不胜感激。
import numpy as np
import pandas as pd
accesskey = 'xxxx'
api = keepaAPI.API(accesskey)
df = pd.read_excel('C:/Users/xxxx.xlsx',
sheet_name = 'abebooks',
header = 0,
index_col = None,
usecols = "A:P",
convert_float = True)
for index, row in df.iterrows():
products = api.ProductQuery(row['xxx'])
product = products[0]
newprice = products[0]['data']['NEW']
newpricetime = products[0]['data']['NEW_time']
usedprice = products[0]['data']['USED']
usedpricetime = products[0]['data']['USED_time']
bsr = products[0]['data']['SALES']
bsrtime = products[0]['data']['SALES_time']
df = pd.DataFrame([[products[0]['title'],
products[0]['asin'],newprice[-1], usedprice[-1], bsr[-1],
products[0]['binding']]])
df2 = pd.DataFrame([], columns=list(["title", "Asin",
"New price", "Used price", "BSR", "Binding"]))
df.append(df2, ignore_index=True)
got there in the end, created a list of dataframes, and concat them at the end of the loop: 最后到达那里,创建一个数据帧列表,并在循环结束时连接它们:
dfa_list = []
import numpy as np
import pandas as pd
accesskey = 'xxxx'
api = keepaAPI.API(accesskey)
df = pd.read_excel('C:/Users/xxxx.xlsx',
sheet_name = 'abebooks',
header = 0,
index_col = None,
usecols = "A:P",
convert_float = True)
for index, row in df.iterrows():
products = api.ProductQuery(row['xxx'])
product = products[0]
newprice = products[0]['data']['NEW']
newpricetime = products[0]['data']['NEW_time']
usedprice = products[0]['data']['USED']
usedpricetime = products[0]['data']['USED_time']
bsr = products[0]['data']['SALES']
bsrtime = products[0]['data']['SALES_time']
df = pd.DataFrame([[products[0]['title'],
products[0]['asin'],newprice[-1], usedprice[-1], bsr[-1],
products[0]['binding']]])
df2 = pd.DataFrame([], columns=list(["title", "Asin",
"New price", "Used price", "BSR", "Binding"]))
dfa_list.append(df2)
it_df = pd.concat(dfa_list)
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