Faster way to make pandas Multiindex dataframe than append

Question

I am looking for a faster way to load data from my json object into a multiindex dataframe.

My JSON is like:

    {
        "1990-1991": {
            "Cleveland": {
                "salary": "$14,403,000",
                "players": {
                    "Hot Rod Williams": "$3,785,000",
                    "Danny Ferry": "$2,640,000",
                    "Mark Price": "$1,400,000",
                    "Brad Daugherty": "$1,320,000",
                    "Larry Nance": "$1,260,000",
                    "Chucky Brown": "$630,000",
                    "Steve Kerr": "$548,000",
                    "Derrick Chievous": "$525,000",
                    "Winston Bennett": "$525,000",
                    "John Morton": "$350,000",
                    "Milos Babic": "$200,000",
                    "Gerald Paddio": "$120,000",
                    "Darnell Valentine": "$100,000",
                    "Henry James": "$75,000"
                },
                "url": "https://hoopshype.com/salaries/cleveland_cavaliers/1990-1991/"
            },

I am making the dataframe like:

    df = pd.DataFrame(columns=["year", "team", "player", "salary"])
    
    for year in nbaSalaryData.keys():
        for team in nbaSalaryData[year]:
            for player in nbaSalaryData[year][team]['players']:
                df = df.append({
                        "year": year,
                        "team": team,
                        "player": player,
                        "salary": nbaSalaryData[year][team]['players'][player]
                    }, ignore_index=True)
    
    df = df.set_index(['year', 'team', 'player']).sort_index()
    df

Which results in:

                                              salary 
    year       team     player
    1990-1991  Atlanta  Doc Rivers          $895,000
                        Dominique Wilkins   $2,065,000
                        Gary Leonard        $200,000
                        John Battle         $590,000
                        Kevin Willis        $685,000
    ... ... ... ...
    2020-2021   Washington  Robin Lopez     $7,300,000
                        Rui Hachimura       $4,692,840
                        Russell Westbrook   $41,358,814
                        Thomas Bryant       $8,333,333
                        Troy Brown          $3,372,840

This is the form I want - year, team, and player as indexes and salary as a column. I know using append is slow but I cannot figure out an alternative. I tried to make it using tuples (with a slightly different configuration - no players and salary) but it ended up not working.

    tuples = []
    index = None

    for year in nbaSalaryData.keys():
        for team in nbaSalaryData[year]:
            t = nbaSalaryData[year][team]
            tuples.append((year, team))

    index = pd.MultiIndex.from_tuples(tuples, names=["year", "team"])
    df = index.to_frame()
    df

Which outputs:

                             year   team
    year    team        
    1990-1991   Cleveland   1990-1991   Cleveland
                New York    1990-1991   New York
                Detroit     1990-1991   Detroit
                LA Lakers   1990-1991   LA Lakers
                Atlanta     1990-1991   Atlanta

I'm not that familiar with pandas but realize there must be a faster way than append().

Pierre D · Accepted Answer · 2021-01-05 05:13:47Z

1

You can adapt the answer to a very similar question as follow:

z = json.loads(json_data)

out = pd.Series({
    (i,j,m): z[i][j][k][m]
    for i in z
    for j in z[i]
    for k in ['players']
    for m in z[i][j][k]
}).to_frame('salary').rename_axis('year team player'.split())

# out:

                                           salary
year      team      player                       
1990-1991 Cleveland Hot Rod Williams   $3,785,000
                    Danny Ferry        $2,640,000
                    Mark Price         $1,400,000
                    Brad Daugherty     $1,320,000
                    Larry Nance        $1,260,000
                    Chucky Brown         $630,000
                    Steve Kerr           $548,000
                    Derrick Chievous     $525,000
                    Winston Bennett      $525,000
                    John Morton          $350,000
                    Milos Babic          $200,000
                    Gerald Paddio        $120,000
                    Darnell Valentine    $100,000
                    Henry James           $75,000

Also, if you intend to do some numerical analysis with those salaries, you probably want them as numbers, not strings. If so, also consider:

out['salary'] = pd.to_numeric(out['salary'].str.replace(r'\D', ''))

PS: Explanation:

The for lines are just one big comprehension to flatten your nested dict. To understand how it works, try first:

[
    (i,j)
    for i in z
    for j in z[i]
]

The 3rd for would be to list all keys of z[i][j], which would be: ['salary', 'players', 'url'], but we are only interested in 'players', so we say so.

The final bit is, instead of a list, we want a dict. Try the expression without surrounding with pd.Series() and you'll see exactly what's going on.

edited Jan 5, 2021 at 5:13

answered Jan 5, 2021 at 4:56

Pierre D

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2 Comments

nonethewiser Over a year ago

Wow, that's a doozy to comprehend at the moment. That's a good post - I will go over it more to try and understand how the series is built. This is very fast, especially compared to what I was doing. Thanks Pierre

Pierre D Over a year ago

sure, I added a tiny bit of an explanation and how to progressively see what the various bits are for.

sammywemmy · Accepted Answer · 2021-01-05 07:57:53Z

We can use the for loop to create the dataframe and append, before finally concatenating: Delaying the concatenation till the end is much better than appending dataframes within the loop

box = []
# data refers to the shared json in the question
for year, value in data.items():
    for team, players in value.items():
        content = players["players"]
        content = pd.DataFrame.from_dict(
            content, orient="index", columns=["salary"]
        ).rename_axis(index="player")
        content = content.assign(year=year, team=team)
        box.append(content)

box

[                       salary       year       team
 player                                             
 Hot Rod Williams   $3,785,000  1990-1991  Cleveland
 Danny Ferry        $2,640,000  1990-1991  Cleveland
 Mark Price         $1,400,000  1990-1991  Cleveland
 Brad Daugherty     $1,320,000  1990-1991  Cleveland
 Larry Nance        $1,260,000  1990-1991  Cleveland
 Chucky Brown         $630,000  1990-1991  Cleveland
 Steve Kerr           $548,000  1990-1991  Cleveland
 Derrick Chievous     $525,000  1990-1991  Cleveland
 Winston Bennett      $525,000  1990-1991  Cleveland
 John Morton          $350,000  1990-1991  Cleveland
 Milos Babic          $200,000  1990-1991  Cleveland
 Gerald Paddio        $120,000  1990-1991  Cleveland
 Darnell Valentine    $100,000  1990-1991  Cleveland
 Henry James           $75,000  1990-1991  Cleveland]

Concatenate and reorder index levels:

(
    pd.concat(box)
    .set_index(["year", "team"], append=True)
    .reorder_levels(["year", "team", "player"])
)

That is an interesting approach. I wouldn't have thought of that.

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