从.json 文件中获取行而不是列的问题

Question

I have several.json files that I would like to combine into one.csv file. The.json files look like this:

{"version":1.3,"people":[{"person_id":[-1],"pose_keypoints_2d":[346.096,136.31,0.822246,339.237,210.457,0.840786,264.747,203.769,0.807762,149.518,183.631,0.854263,88.6763,82.0749,0.906274,400.122,210.38,0.854152,508.225,183.228,0.898651,555.846,75.2072,0.958429,345.636,345.892,0.111406,291.626,346.153,0.194191,0,0,0,0,0,0,393.258,346.08,0.15249,0,0,0,0,0,0,339.241,136.084,0.804675,352.463,136.006,0.702916,305.539,156.42,0.639209,359.362,156.248,0.325405,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
"face_keypoints_2d":[],"hand_left_keypoints_2d":[],"hand_right_keypoints_2d":[],"pose_keypoints_3d":[],
"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]}

The code I use to load and combine files:

import json
import os
import pandas as pd

data = []

for file in os.listdir("C:/Users/ciach/PycharmProjects/CSV/"):
    if file.endswith(".json"):
        data2 = json.load(open(os.path.join("C:/Users/ciach/PycharmProjects/CSV/", file)))
        df = pd.json_normalize(data2,record_path=['people','pose_keypoints_2d'])
        data.append(df)

temp = pd.concat(data, ignore_index = True) 
temp.to_csv("keypoints.csv", index=False) 

After compilation with two files, I get the following results in.csv file (all values are in one column):

338.938
156.226
...
0.000000
0.000000

I would like to get the values only from the list 'pose_keypoints_2d' in one row for each.json file instead of one whole column from all files. Something like that:

338.938000 156.226000 0.322833 ...  0.000000  0.000000
346.096000 136.310000 0.822246 ...  0.000000  0.000000

Answer 1

You df is a column of 75 rows when it's added to data2.

Try replacing this:

data.append(df)

by this

data.append(df.T)

Which (df.T) is one row with 75 columns