使用 pandas 循环读取CSV文件，然后将它们串联 [英] Reading CSV files in a loop using pandas, then concatenating them

问题描述

我有10个csv文件，分别名为data_run1_all.csv，data_run2_all.csv，...，data_run10_all.csv. CSV文件具有相同的列，但具有不同的行.

I have 10 csv files, named data_run1_all.csv, data_run2_all.csv, ..., data_run10_all.csv. CSV files have same columns, but different rows.

现在，我将它们逐一导入到df_run1，df_run2，...，df_run10.

Now I am importing them one by one to df_run1, df_run2, ..., df_run10.

我可以使用循环导入它们吗?类似于i=1 to 10, df_runi=pandas.read_csv('data_runi_all.csv').

Can I use a loop to import them? Something like: i=1 to 10, df_runi=pandas.read_csv('data_runi_all.csv').

我之所以问是因为每个数据框的数据分析，绘图等也是相同的.每个数据帧的所有代码重复10次.如果我可以使用循环执行10次，则代码将更短并且可读性更高.

I am asking because the data analysis, plotting, etc. for each data frame are same, too. All the code for each data frame is repeated 10 times. If I can use a loop to do 10 times, the code will be much shorter and readable.

推荐答案

循环读取您的CSV并调用pd.concat:

Read your CSVs in a loop and call pd.concat:

file_name = 'data_run{}_all.csv'
df_list = []
for i in range(1, 11):
    df_list.append(pd.read_csv(file_name.format(i))

df = pd.concat(df_list)

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或者，您可以在理解范围内构建列表:

---

Alternatively, you could build the list inside a comprehension:

file_name = 'data_run{}_all.csv'
df = pd.concat([pd.read_csv(file_name.format(i)) for i in range(1, 11)])

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