Python loop to split a dataframe by a particular row/index for INSERT into SQL server

user_10530575 0
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I have a dataframe with a couple thousand rows, I want to create a loop to split the entire dataframe by 90 rows each sub-dataframe and INSERT each subset into SQL server.

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my dummy way to split it by a fixed number 90 rows which is not efficient

df1 = df.loc[0:89,:]
df1.to_sql("tableName", schema='dbo', con=engine, method='multi')

df2 = df.loc[90:179,:]
df2.to_sql("tableName", schema='dbo', con=engine, method='multi')
......

sample data

df = pd.DataFrame(np.random.randint(0,100,size=(2000, 4)), columns = ['Name', 'Age','food','tree']) #size control how many rows

because of my sql server has the limitation, I can only insert 90 rows for each Bulk Insert.

pandas pyodbc dataframe python-3.x python
3 Answers

np.array_split(arr, indices)

Split an array into multiple sub-arrays using the given indices.

for chunk in np.array_split(df, range(90, len(df), 90)):
    INSERT_sql()
user_128331660

Here's a pretty verbose approach. In this case, taking your sample dataframe, it is sliced in increments of 90 rows. The first block will be 0-89, then 90-179, 180-269, etc.

import pandas as pd
import numpy as np
import math

df = pd.DataFrame(np.random.randint(0,100,size=(2000, 4)), columns = ['Name', 'Age','food','tree']) #size control how many rows

def slice_df(dataframe, row_count):

    num_rows = len(dataframe)
    num_blocks = math.ceil(num_rows / row_count)
    
    for i in range(num_blocks):
        df = dataframe[(i * row_count) : ((i * row_count)+row_count-1)]
        # Do your insert command here

slice_df(df, 90)
user_10155630

I believe this should work:

for i in range(0,len(df),90):
    df.iloc[i:i+90].to_sql("tableName", schema='dbo', con=engine, method='multi')
user_183445120
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