python - 获取 pandas 中多个数据帧的每个第 n 个元素

Question

我有 10 个具有相同结构的数据帧，全部包含 10000 条记录。我想创建一个包含所有不同数据帧的每 1000 条记录的矩阵。

所以我的数据集如下:

df = pd.read_csv('10000_0.csv')
df1 = pd.read_csv('10000_1.csv')
df2 = pd.read_csv('10000_2.csv')
df3 = pd.read_csv('10000_3.csv')
df4 = pd.read_csv('10000_4.csv')
df5 = pd.read_csv('10000_5.csv')
df6 = pd.read_csv('10000_6.csv')
df7 = pd.read_csv('10000_7.csv')
df8 = pd.read_csv('10000_8.csv')
df9 = pd.read_csv('10000_9.csv')

现在我想创建一个数组，其中 [] 第一个元素是 [df['name'][1000], df1['name'][1000], ..., df9[ 'name'][1000]]，是否可以在 pandas 中有效地构造这个？

Answer 1

用途:

files = ['10000_{}.csv'.format(x) for x in range(10)]

#list of all DataFrames
dfs = [pd.read_csv(f) for f in files]

#list of one row DataFrame 
L = [x.iloc[[1000]] for x in dfs]
#list of Series
L = [x.iloc[1000] for x in dfs]

#final DataFrame
df1 = pd.concat(L, ignore_index=True)

如果只需要一行，另一种解决方案:

files = ['10000_{}.csv'.format(x) for x in range(10)]

#list of all DataFrames
dfs = [pd.read_csv(f, skiprows=(1, 1000), nrows=1) for f in files]