如何将长度为365的列表循环拆分为多个Pandas DataFrame

Got it, let's work through this. You've got a list d with 365 elements, each holding table-like data (with columns like No, Name and their corresponding rows), and you want to automate converting each element into its own Pandas DataFrame (like df1 for d[1]). Here are two practical approaches:

First, a quick note on indexing #

Python lists use 0-indexing, so your 365-element list runs from d[0] (first element) to d[364] (last). If you specifically want df1 to map to d[1], double-check that your list has enough elements (or adjust the code below to map df1 to d[0]—that's more aligned with Python's default behavior).

Approach 1: Create individual DataFrame variables (not recommended for large sets) #

If you really need separate variables like df1, df2, etc., you can use Python's globals() function to dynamically create them. Just keep in mind: managing 365 separate variables will get messy fast.

import pandas as pd

# Loop through the list and create df1, df2...df365
for idx in range(1, len(d) + 1):
    # Map df1 to d[0], df2 to d[1], etc.
    # To map df1 to d[1], replace d[idx-1] with d[idx] (ensure idx doesn't exceed len(d)-1)
    globals()[f"df{idx}"] = pd.DataFrame(d[idx-1])

Approach 2: Store DataFrames in a dictionary (highly recommended) #

This is the cleaner, more maintainable option. Instead of scattering variables everywhere, you'll store all DataFrames in a single dictionary where keys are names like df1 and values are the corresponding DataFrames.

import pandas as pd

# Initialize an empty dictionary to hold your DataFrames
dfs_dict = {}

# Populate the dictionary
for idx in range(1, len(d) + 1):
    dfs_dict[f"df{idx}"] = pd.DataFrame(d[idx-1])

# Access individual DataFrames like this:
# dfs_dict["df1"]  # Gets the DataFrame from d[0]
# dfs_dict["df365"]  # Gets the DataFrame from d[364]

Bonus: If your elements are raw text tables #

If the elements in d are string-formatted tables (not already structured data like lists of dictionaries), you can parse them using StringIO to read them as CSV-like data:

from io import StringIO

for idx in range(1, len(d) + 1):
    # Example: d[idx-1] is a string like "No,Name\n1,Alice\n2,Bob"
    dfs_dict[f"df{idx}"] = pd.read_csv(StringIO(d[idx-1]))

内容的提问来源于stack exchange，提问作者Ahamed Moosa