[英]Appending Rows to a data frame
I am stuck on a simple task. 我被困在一个简单的任务上。 I want to create an empty DataFrame and append rows to it based on a query of another dataset.
我想创建一个空的DataFrame并根据另一个数据集的查询向其追加行。 I have tried the answers here but I am missing something ..beginner Pythoner.
我在这里尝试了答案,但我缺少Python初学者。 Any help would be appreciated.
任何帮助,将不胜感激。 I want to take the top 3 rows of each state and add them into a new dataframe for processing.
我想获取每个状态的前3行,并将它们添加到新的数据框中进行处理。 I tried to append also..
我也尝试附加。
def test():
#get the list of states
states_df = census_df.STNAME.unique()
population_df = pd.DataFrame()
for st in states_df:
temp_df = pd.DataFrame(census_df[census_df['STNAME'] == st].nlargest(3,'CENSUS2010POP'))
pd.concat([temp_df, population_df], ignore_index = True)
return 1
I think I know what course you're doing, I had a great time with that a year ago, keep it up! 我想我知道您在做什么,一年前我度过了愉快的时光,继续努力!
The simplest/fastest way I've found to concatenate a bunch of sliced dataframes is to append each df to a list, then at the end just concatenate that list. 我发现连接一堆切片数据帧的最简单/最快的方法是将每个df附加到列表中,然后最后仅连接该列表。 See the working code below (it does what I interpret you meant).
请参阅下面的工作代码(它符合我的解释)。
I agree with David's suggestion on sorting, easier to use sort and then just slice the first 3. As nlargest() works on and returns a Series I believe and not a dataframe, whereas you want to keep the whole dataframe structure (all the columns) for concatenation. 我同意David关于排序的建议,使用起来比较容易,然后只对第一个3进行切片。当nlargest()处理并返回一个我相信的Series而不是一个dataframe时,而您想保留整个dataframe结构(所有列) )进行串联。
Also why is your function returning 1? 同样为什么您的函数返回1? Typo?
错字? I guess you want to return your desired output if you're putting it in a function, so I changed that too.
我想如果要将其放入函数中,则想返回所需的输出,因此我也进行了更改。
import pandas as pd
import numpy as np
#create fake data random numbers
data = np.random.randint(2,11,(40,3))
census_df = pd.DataFrame(index=range(40), columns=['Blah', 'Blah2','CENSUS2010POP'], data=data)
#create fake STNAME column
census_df['STNAME'] = list('aaaabbbbccccddddeeeeffffgggghhhhiiiijjjj')
#Function:
def test(census_df):
states_list = census_df.STNAME.unique() #changed naming to _list as it's not a df.
list_of_dfs = list() #more efficient to append each df to a list
for st in states_list:
temp_df = census_df[census_df['STNAME']==st]
temp_df = temp_df.sort_values(by=['CENSUS2010POP'], ascending=False).iloc[:3]
list_of_dfs.append(temp_df)
population_df = pd.concat(list_of_dfs,ignore_index=True)
return population_df
population_df = test(census_df)
Welcome to SO! 欢迎来到SO! Is your problem appending or the top three rows?
是附加问题还是前三行?
For append, try the df.append function. 对于追加,请尝试df.append函数。 It could look something like:
它可能看起来像:
#get the list of states
states_df = census_df.STNAME.unique()
population_df = pd.DataFrame()
for st in states_df:
temp_df = pd.DataFrame(census_df[census_df['STNAME'] == st].nlargest(3,'CENSUS2010POP'))
population_df = population_df.append(temp_df, ignore_index = True) #append the temp df to your main df, ignoring the index
For the top rows you could us df.sort_values(by=['column name'],ascending=False) and then select the top three rows: 对于最上面的行,您可以使用df.sort_values(by = [''column name'],ascending = False),然后选择最上面的三行:
population_df = population_df.append(temp_df[0:3], ignore_index = True)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.