I have this data of 35k entries, I want to track only those Event_Action entries which have 0 in Event_Label and export them. Condition is : if an entry 100-genuine-data-entry-jobs-without-investment:us has 0 return all results and 1099-sales-jobs:us does't contain 0 , so we dont need this.
What have I tried so far is first
- filter all Event_Action entries which have 0 against them and create a new sheet/table
df1 = pd.read_excel(pwd + "/zero_instance.xlsx")
df2 = pd.read_excel(pwd + "/All Last 7 days data rcom.xlsx")
- then
df3 = df1.merge(df2, how='left', left_on='Event_Action', right_on='Event_Action')
But its returning me only first instances of each row. I need all instances when condition is met.
Here's the sample of the data I'm working on.
| Event_Action | Event_Label |
|---|---|
| 100-genuine-data-entry-jobs-without-investment:us | 0 |
| 100-genuine-data-entry-jobs-without-investment:us | 20000 |
| 100-genuine-data-entry-jobs-without-investment:us | 42323 |
| 100-genuine-data-entry-jobs-without-investment:us | 72639 |
| 100-genuine-data-entry-jobs-without-investment:us | 73898 |
| 100-genuine-online-data-entry-jobs:us | 122299 |
| 1099-bookkeeping-jobs:us | 0 |
| 1099-bookkeeping-jobs:us | 568 |
| 1099-bookkeeping-jobs:us | 2004 |
| 1099-bookkeeping-jobs:us | 3001 |
| 1099-sales-jobs:us | 1721341 |
| 1099-sales-jobs:us | 1779187 |
| 10th-pass:in | 3870 |
| 1120:in | 11 |
| 1120:in | 25 |
| 12-pass:in | 474 |
| 12th-pass:in | 7 |
| 12th-pass:in | 230 |
| 12th-pass:in | 4469 |
| 12th-pass-best:in | 143 |
| 12th-pass-fresher-jobs:us | 703 |
| 12th-pass-patanjali:in | 0 |
| 12th-pass-patanjali:in | 97 |
| 12th-pass-patanjali:in | 102 |