Pandas try to append a row to a dataframe but keeps overwriting the existing row

Question

My dataframe (df_UP) looks like this:

 Total_trends #up #down #flat 0.05 811 326 310 175

I am using a jupyter notebook, when I try to append a row with the following code:

`d = {'Total_trends': [total.time], '#up': [good_trigger.time], '#down': [bad_trigger.time], '#flat': [flat.time]} df_UP.append(d, ignore_index=True)`

It works the first time:

Total_trends #up #down #flat 0 811 326 310 175 1 [811] [326] [310] [175]

But when I run again the cell with other values in d, it just overwrites the existing data at row 1 with the new one, any idea why? Thanks!

For me it working perfectly, maybe need df_UP = df_UP.append(pd.DataFrame(d), ignore_index=True) ? — jezrael
– jezrael, Commented Feb 28, 2019 at 11:12
@jezrael, yup the following: df_UP = df_UP.append(pd.DataFrame(d), ignore_index=True) works! you can add it as answer, I will validate it! thanks! — Viktor.w
– Viktor.w, Commented Feb 28, 2019 at 11:15

jezrael · Accepted Answer · 2019-02-28 11:17:20Z

You can convert dictionary to DataFrame and assign back:

d = {'Total_trends': [10], '#up': [20], '#down': [3], '#flat': [0]} df_UP = df_UP.append(pd.DataFrame(d), ignore_index=True) print (df_UP) Total_trends #up #down #flat 0 811 326 310 175 1 10 20 3 0 d = {'Total_trends': [160], '#up': [270], '#down': [63], '#flat': [30]} df_UP = df_UP.append(pd.DataFrame(d), ignore_index=True) print (df_UP) Total_trends #up #down #flat 0 811 326 310 175 1 10 20 3 0 2 160 270 63 30

Collectives™ on Stack Overflow

Pandas try to append a row to a dataframe but keeps overwriting the existing row

1 Answer 1

Comments

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Related