Python：使用熊猫重新格式化列表列表

Question

Given a list of lists that I am extracting from an API. I managed to reformat this data using pandas. However I am trying to get the averages of this data then have it print out line by line instead of in a side by side format.

{
    "resource": "playergamelog",
    "parameters": {
        "PlayerID": 201939,
        "LeagueID": "00",
        "Season": "2014-15",
        "SeasonType": "Regular Season"
    },
    "resultSets": [
        {
            "name": "PlayerGameLog",
            "headers": [
                "SEASON_ID",
                "Player_ID",
                "Game_ID",
                "GAME_DATE",
                "MATCHUP",
                "WL",
                "MIN",
                "FGM",
                "FGA",
                "FG_PCT",
                "FG3M",
                "FG3A",
                "FG3_PCT",
                "FTM",
                "FTA",
                "FT_PCT",
                "OREB",
                "DREB",
                "REB",
                "AST",
                "STL",
                "BLK",
                "TOV",
                "PF",
                "PTS",
                "PLUS_MINUS",
                "VIDEO_AVAILABLE"
            ],
            "rowSet": [
                [
                    "22014",
                    201939,
                    "0021401229",
                    "APR 15, 2015",
                    "GSW vs. DEN",
                    "W",
                    19,
                    4,
                    10,
                    0.4,
                    2,
                    4,
                    0.5,
                    0,
                    0,
                    0,
                    2,
                    2,
                    4,
                    7,
                    5,
                    0,
                    2,
                    1,
                    10,
                    14,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401212",
                    "APR 13, 2015",
                    "GSW vs. MEM",
                    "W",
                    29,
                    6,
                    10,
                    0.6,
                    3,
                    6,
                    0.5,
                    0,
                    0,
                    0,
                    0,
                    4,
                    4,
                    8,
                    1,
                    0,
                    3,
                    2,
                    15,
                    24,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401192",
                    "APR 11, 2015",
                    "GSW vs. MIN",
                    "W",
                    35,
                    11,
                    21,
                    0.524,
                    5,
                    11,
                    0.455,
                    7,
                    8,
                    0.875,
                    0,
                    4,
                    4,
                    7,
                    4,
                    0,
                    3,
                    1,
                    34,
                    17,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401174",
                    "APR 09, 2015",
                    "GSW vs. POR",
                    "W",
                    35,
                    17,
                    23,
                    0.739,
                    8,
                    13,
                    0.615,
                    3,
                    3,
                    1,
                    1,
                    1,
                    2,
                    10,
                    0,
                    0,
                    4,
                    2,
                    45,
                    21,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401156",
                    "APR 07, 2015",
                    "GSW @ NOP",
                    "L",
                    35,
                    9,
                    18,
                    0.5,
                    5,
                    8,
                    0.625,
                    2,
                    2,
                    1,
                    1,
                    5,
                    6,
                    9,
                    1,
                    0,
                    2,
                    3,
                    25,
                    4,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401150",
                    "APR 05, 2015",
                    "GSW @ SAS",
                    "L",
                    30,
                    9,
                    17,
                    0.529,
                    5,
                    10,
                    0.5,
                    1,
                    2,
                    0.5,
                    0,
                    4,
                    4,
                    6,
                    1,
                    0,
                    4,
                    2,
                    24,
                    -16,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401142",
                    "APR 04, 2015",
                    "GSW @ DAL",
                    "W",
                    27,
                    4,
                    12,
                    0.333,
                    1,
                    4,
                    0.25,
                    2,
                    3,
                    0.667,
                    2,
                    2,
                    4,
                    3,
                    1,
                    0,
                    1,
                    1,
                    11,
                    9,
                    1
                ],
                [
                    "22014",
                    201939,
                    "0021401126",
                    "APR 02, 2015",
                    "GSW vs. PHX",
                    "W",
                    34,
                    10,
                    22,
                    0.455,
                    6,
                    11,
                    0.545,
                    2,
                    2,
                    1,
                    1,
                    7,
                    8,
                    5,
                    0,
                    0,
                    6,
                    3,
                    28,
                    -1,
                    1
                ]
            ]
        }
    ]
}

My code prints out like the following:

FG3A                       3.83
FG3_PCT                    0.34
FTM                        5.50
FTA                        7.17
FT_PCT                     0.78
OREB                       0.75

When I recieve the above response from the API. I assign

data= response_shots.json()['resultSets'][0]['rowSet'] #stats is a list
# Then I assign/store the headers obtained from this API:
headers_traditional = data['resultSets'][0]['headers'] 
# Then I take list of lists from data and reformat it using the following:
traditional_stats = pd.DataFrame(data, columns=headers_traditional)
# If I want to print the traditional stats with mean I use:
print(traditional_stats.mean())

But whenever this is done or I have more than one player I am evaluating, it prints the next players results below. I'd rather have them print side by side or all of one players averages on one line then move to the next player (while still using the same headers)

Answer 1

Oh wait, I just read your comment. It should be easy to do what you want:

Case when all players are in the same DataFrame

averages = df.groupby('Player_ID').mean().unstack()

Case when each player is in a separate DataFrame

Here I am assuming that you are getting each player one after another, looping over each Player_ID.

averages = pd.DataFrame()
for player in all_player_ids:
    # p_data = get data for Player_ID == player from API
    # df = whatever you do to clean p_data
    averages = averages.append(df.groupby('Player_ID').mean().unstack())

I am guessing you are only using the data from p_data['resultSets'] . Try it and let me know.