How to update a Postgres table column using a pandas data frame?

Question

I am adding a single column to a Postgres table with 100+ columns via Django ( a new migration). How can I update a column in a PostgreSQL table with the data from a pandas data_frame? The pseudo-code for Postgres SQL UPDATE would be:

UPDATE wide_table wt
SET wt.z = df.z
WHERE date = 'todays_date'

The reason for doing it this way is that I am computing a column in the data_frame using a CSV that is in S3 (this is df.z). The docs for Postgres update are straightforward to use, but I am unsure how to do this via Django, sqlalchemy, pyodbc, or the like.

I apologize if this is a bit convoluted. A small and incomplete example would be:

Wide Table (pre-update column z)

identifier    |      x       |      y      |      z       |      date       
foo           |      2       |      1      |     0.0      |      ...           
bar           |      2       |      8      |     0.0      |      ...      
baz           |      3       |      7      |     0.0      |      ...      
foo           |      2       |      8      |     0.0      |      ...      
foo           |      1       |      5      |     0.0      |      ...      
baz           |      2       |      8      |     0.0      |      ...      
bar           |      9       |      3      |     0.0      |      ...      
baz           |      2       |      3      |     0.0      |      ...      

Example Python snippet

def apply_function(identifier):
    # Maps baz-> 15.0, bar-> 19.6, foo -> 10.0 for single date
    df = pd.read_csv("s3_file_path/date_file_name.csv")
    # Compute 'z' based on identifier and S3 csv
    return z

postgres_query = "Select identifier from wide_table"
df = pd.read_sql(sql=postgres_query, con=engine)
df['z'] = df.identifier.apply(apply_function)

# Python / SQL Update Logic here to update Postgres Column
???

Wide Table (post-update column z)

identifier    |      x       |      y      |      z        |      date 
foo           |      2       |      1      |     10.0      |      ...     
bar           |      2       |      8      |     19.6      |      ... 
baz           |      3       |      7      |     15.0      |      ... 
foo           |      2       |      8      |     10.0      |      ... 
foo           |      1       |      5      |     10.0      |      ... 
baz           |      2       |      8      |     15.0      |      ... 
bar           |      9       |      3      |     19.6      |      ... 
baz           |      2       |      3      |     15.0      |      ... 

NOTE: The values in z will change daily so simply creating another table to hold these z values is not a great solution. Also, I'd really prefer to avoid deleting all of the data and adding it back.

Answer 1

Ran into a similar problem and the current accepted solution was too slow for me. My table had 500k+ rows and i needed to update 100k+ rows. After lengthy research and trial and error i arrived at an efficient and correct solution.

The idea is to use psycopg as your writer and to use a temp table. df is your pandas dataframe that contains values you want to set.

import psycopg2

conn = psycopg2.connect("dbname='db' user='user' host='localhost' password='test'")
cur = conn.cursor()

rows = zip(df.id, df.z)
cur.execute("""CREATE TEMP TABLE codelist(id INTEGER, z INTEGER) ON COMMIT DROP""")
cur.executemany("""INSERT INTO codelist (id, z) VALUES(%s, %s)""", rows)

cur.execute("""
    UPDATE table_name
    SET z = codelist.z
    FROM codelist
    WHERE codelist.id = vehicle.id;
    """)

cur.rowcount
conn.commit()
cur.close()
conn.close()