将自定义函数应用于pandas数据框中的每一行的更快方法？

Question

I have two dataframes ad_df, x_df . I created a function find_ids that takes in an ID ad_id and a date ad_date from ad_df .

The function filters x_df by the following

• x_df['ID] = ad_id
• x_df['Last_Date'] is between 2 days before ad_date and 15 days after ad_date
• In at least one of the rows from the beginning range of dates and the end range of dates, x_df['Geo_Label'] contains a '1'

Then I append the resulting dataframe to a global dataframe res_df that keeps track of these rows.

I call the function by using the line below:

ad_df.apply(lambda x: find_units_moved(x['SerialNo'],x['Audit Date'] ), axis = 1)

Is there a faster way to do this? ad_df has about 1M rows, so hopefully there is a faster way to do this. The code for the function is shown below.

def find_ad_ids(ad_id, ad_date):
    id_specific_df = x_df.loc[x_df['ID'] == ad_id]

    beg_range_date = ad_date - timedelta(days = 2)
    end_range_date = ad_date + timedelta(days = 15)

    beg_df = id_specific_df[(id_specific_df['Last_Date'] > beg_range_date) & (id_specific_df['Last_Date'] < ad_date)]
    end_df = id_specific_df[(id_specific_df['Last_Date''] > ad_date) & (id_specific_df['Last_Date'] < end_range_date)]


    if(len(beg_df.columns) != 0 and len(end_df.columns) != 0):
        if(('1' in beg_df['Geo_Label'].array) and ('1' in end_df['Geo_Label'].array)):
            res_df.append(pd.concat([beg_df, end_df], ignore_index=True))

Answer 1

One of the fastest ways to append data to a Dataframe is through dict:

startTime = time.perf_counter()
row_list = []
for i in range (0,5):
    row_list.append(dict( (a,np.random.randint(100)) for a in ['A','B','C','D','E']))
for i in range( 1,numOfRows-4):
    dict1 = dict( (a,np.random.randint(100)) for a in ['A','B','C','D','E'])
    row_list.append(dict1)

df4 = pd.DataFrame(row_list, columns=['A','B','C','D','E'])
print('Elapsed time: {:6.3f} seconds for {:d} rows'.format(time.perf_counter() - startTime, numOfRows))
print(df4.shape)