Pandas - converting dtypes

Question

I have these two dataframes I need to merge:

df_melt :

dtype: object     MatchID GameWeek        Date                      Team  Home               AgainstTeam
0     46605        1  2019-08-09                 Liverpool  Home              Norwich City
1     46605        1  2019-08-09              Norwich City  Away                 Liverpool
2     46606        1  2019-08-10           AFC Bournemouth  Home          Sheffield United
3     46606        1  2019-08-10          Sheffield United  Away           AFC Bournemouth
4     46607        1  2019-08-10                   Burnley  Home               Southampton
..      ...      ...         ...                       ...   ...                       ...
533   46871       27  2020-02-23                   Watford  Away         Manchester United
534   46872       27  2020-02-22          Sheffield United  Home  Brighton and Hove Albion
535   46872       27  2020-02-22  Brighton and Hove Albion  Away          Sheffield United
536   46873       27  2020-02-22               Southampton  Home               Aston Villa
537   46873       27  2020-02-22               Aston Villa  Away               Southampton

df_pm :

dtype: object                                        Player  GameWeek  Minutes  ... CloseShotCreated TotalShotCreated  HeadersCreated
PlayerMatchesDetailID                                             ...                                                  
1                                     Alisson         1       90  ...                0                0               0
2                             Virgil van Dijk         1       90  ...                0                0               0
3                                Joseph Gomez         1       90  ...                0                1               0
4                            Andrew Robertson         1       90  ...                0                1               0
5                      Trent Alexander-Arnold         1       90  ...                3                3               1
...                                       ...       ...      ...  ...              ...              ...             ...
15053                             Matty James        22        0  ...                0                0               0
15054                             Matty James        23        0  ...                0                0               0
15055                             Matty James        24        0  ...                0                0               0
15056                             Matty James        25        0  ...                0                0               0
15057                             Matty James        26        0  ...                0                0               0

This is how I was trying to perform the merge:

#Instantiate an empty list
match_ids = []
home_away = []
dates = []

#For each row in the player matches dataframe...
for row in df_pm.itertuples():
    #Look up the match id from the team matches dataframe
    team = row.ForTeam
    againstteam = row.AgainstTeam
    gameweek = row.GameWeek

    match_id = df_melt.loc[(df_melt['GameWeek']==gameweek)
                          &(df_melt['Team']==team)
                          &(df_melt['AgainstTeam']==againstteam),
                          'MatchID'].item()
    print ('MATCH',match_id)

    date = df_melt.loc[(df_melt['GameWeek']==gameweek)
                          &(df_melt['Team']==team)
                          &(df_melt['AgainstTeam']==againstteam),
                          'Date'].item()

    home = df_melt.loc[(df_melt['GameWeek']==gameweek)
                          &(df_melt['Team']==team)
                          &(df_melt['AgainstTeam']==againstteam),
                          'Home'].item()

    #Add it to the list
    match_ids.append(match_id)
    home_away.append(home)
    dates.append(date)

But I was getting:

Traceback (most recent call last):
  File "tableau_data_generation.py", line 161, in <module>
    'MatchID'].item()
  File "/Users/me/anaconda2/envs/data_science/lib/python3.7/site-packages/pandas/core/base.py", line 652, in item
    return self.values.item()
ValueError: can only convert an array of size 1 to a Python scalar

---

Suggesting that maybe some row was not there. But after printing the whole dataframe, I see that there is no flawed data.

But when I check for types, I see:

df_melt :

MatchID        object
GameWeek       object
Date           object
Team           object
Home           object
AgainstTeam    object

df_pm :

Player                 object
GameWeek                int64
Minutes                 int64
ForTeam                object
AgainstTeam            object
Goals                   int64
ShotsOnTarget           int64
ShotsInBox              int64
CloseShots              int64
TotalShots              int64
Headers                 int64
GoalAssists             int64
ShotOnTargetCreated     int64
ShotInBoxCreated        int64
CloseShotCreated        int64
TotalShotCreated        int64
HeadersCreated          int64

I guess this mismatch must be the culprit...

---

What is the best way of fixing this and converting mismatching types?

---

Note for solution provided:

later, I need to perform the following assignments:

def pos_lookup(x):
    return df_player_basic.loc[df_player_basic['CommentName']==x,
                               'Position'].item()

#Declare the list as a column in the player matches df
df_pm['MatchID']=match_ids
df_pm['Date']=pd.to_datetime(dates)
df_pm['Home']=home_away
df_pm['Position']=df_pm['Player'].map(pos_lookup)

Answer 1

To convert a column of dataframe:

Df[column_name]=Df[column_name].astype(datatype)

ie. 'datatype' being int , str , float etc