从 Json Python 中提取多个键值

Question

I am trying to write code for a project that pulls multiple pieces of data from a JSON API. I've got the code written where it will narrow it down to the segment I want and pull a full list of a given key (siteSlatePlayerId). However, I am trying to get it to also pull 'name' at the same time.

Example: 100031, Lebron James

How can I change this loop (or if you recommend another way) to pull 'siteSlatePlayerId' and 'name' at the same time? Any help would be greatly appreciated.

df = pd.read_json('https://resultsdb-api.rotogrinders.com/api/slates?start=11/03/2019')
df2 = df
df3 = df2[(df2['slateTypeName'] == 'Classic') & (df2['sport'] == 3)]
#df3.to_csv("output3.csv")

Player_Slate = pd.DataFrame.from_dict(df3["slatePlayers"])
print(Player_Slate)

###########################################################
# Get Unique ID for player
###########################################################
def get_playerid(data, key):
   for key in data:
       try:
           if (key == 'siteSlatePlayerId'):
               return (data[key])
       except:
           print('error')
   return 0

for siteSlatePlayerId in Player_Slate.slatePlayers:
   playerid = [get_playerid(x, "siteSlatePlayerId") for x in siteSlatePlayerId]
print(playerid)

This gives me all the player IDs: ['13689965', '13689969', '13689973', '13689977', '13689982', '13689986', '13689989', '13689993', '13689997', '13690002', '13690005', '13690007', '13690011', '13690013', '13690018', '13690022', '13690026', '13690028', '13690030', '13690034', '13690037', '13690041', '13690045', '13690050', '13690054', '13690059', '13690062', '13690066', '13690070', '13690074', '13690078', '13690081', '13690084', '13690088', '13690092', '13690096', ...

Answer 1

First, obtain a clean df:

players_df = pd.json_normalize(df3['slatePlayers'].apply(lambda x: x[0]))

Or players_df = pd.json_normalize(df['slatePlayers'].str[0]) if you prefer. pandas.json_normalize normalize semi-structured JSON data into a flat table.

Then, you can get what you want:

>>> players_df[['name','siteSlatePlayerId']]
    name    siteSlatePlayerId
0   Kawhi Leonard   13691028
1   Lamar Jackson   13676980
2   Lamar Jackson   13680443
3   Ezekiel Elliott 13679976
4   Ezekiel Elliott 13680092

---

Full code:

import pandas as pd
df = pd.read_json('https://resultsdb-api.rotogrinders.com/api/slates?start=11/03/2019')
df2 = df
df3 = df2[(df2['slateTypeName'] == 'Classic') & (df2['sport'] == 3)]
df3.to_csv("output3.csv")
players_df = pd.json_normalize(df['slatePlayers'].apply(lambda x: x[0]))
players_df[['name','siteSlatePlayerId']]