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be5dfa6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # import pandas as pd
#
# pd.set_option("display.max_colwidth", None)
# pd.set_option("display.max_columns", None)
# pd.set_option("display.width", None)
#
# # Load parquet file
# df = pd.read_parquet("/export/home/becky/verl-tool-mm-deepsearch/data/mm_deepsearch/fvqa_qw3vl/val_new.parquet")
#
# # Show first 5 rows
# print(df["images"].head(100))
#
# # Show basic info
# print(df.info())
import pandas as pd
import numpy as np
path = "/export/home/becky/verl-tool-mm-deepsearch/data/mm_deepsearch/fvqa_qw3vl/train_filtered.parquet"
prefix = "/export/home/becky/verl-tool-mm-deepsearch/"
# df = pd.read_parquet(path)
# def update_image_paths(image_array):
# if isinstance(image_array, np.ndarray):
# image_array = image_array.tolist() # convert to list
#
# if isinstance(image_array, list):
# for item in image_array:
# if isinstance(item, dict) and "image" in item:
# # avoid double prefix
# if not item["image"].startswith(prefix):
# item["image"] = prefix + item["image"]
#
# return image_array
#
# df["images"] = df["images"].apply(update_image_paths)
# # save
# df.to_parquet(path, index=False)
pd.set_option("display.max_colwidth", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
# Load parquet file
df = pd.read_parquet(path)
# Show first 5 rows
print(df["images"].head(100))
# Show basic info
print(df.info()) |