[英]Dynamically append dataframes in pandas
I want to load files from a list, calculate mean, median and standard deviation for each row of each file and then create a dataframe listing all the newly calculated fields.我想从列表中加载文件,计算每个文件每一行的平均值、中值和标准差,然后创建一个 dataframe 列出所有新计算的字段。
I have the following code:我有以下代码:
#list files to load
file_names = ["file_1", "file_2", ...]
#empty df
data = pd.DataFrame()
#for loop
for filename in file_names:
df = pd.read_csv(filename, index_col=False, header=0)
mean = df.mean(axis = 1)
median = df.median(axis = 1)
std = df.std(axis = 1)
df = pd.concat([mean, median, std], axis = 1, ignore_index = 1)
data = pd.concat(df, axis=1)
I'm getting an error:我收到一个错误:
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"
Individual dfs that are being created in the for loop look exactly how I want it but I can't concatenate them all together.在 for 循环中创建的单个 dfs 看起来完全符合我的要求,但我无法将它们全部连接在一起。
As it is you're overwriting df
every time through the loop.因为它是你每次通过循环覆盖
df
。
Instead collect the DataFrames in a list, then concatenate that list together.而是将 DataFrames 收集到一个列表中,然后将该列表连接在一起。
df_list = []
#for loop
for filename in file_names:
df = pd.read_csv(filename, index_col=False, header=0)
mean = df.mean(axis = 1)
median = df.median(axis = 1)
std = df.std(axis = 1)
df = pd.concat([mean, median, std], axis = 1, ignore_index = 1)
df_list.append(df)
data = pd.concat(df_list, axis=1)
Change this line更改此行
data = pd.concat(df, axis=1)
to至
data = pd.concat([data,df], axis=1)
should work.. do let me know in any case pls应该工作..无论如何请让我知道
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