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empty row in pandas dataframe - rwahdan - Jun-22-2021
Hi,
I have a data frame that need to add empty line at the end of that. I also see from examples that it show NaN but I don't want that, I need it totally empty. I took and example code below that adds 3 empty lines with "NaN". What I want is only one empty row at the end of the list without "NaN".
import pandas as pd
df_new = pd.DataFrame([])
items = pd.DataFrame([
["Item Type","Item Name","Price AED"],
["laptops", "HP", 2400],
["laptops", "DELL", 3400],
["laptops", "ACER", 1400]
])
for i, row in items.iterrows():
df_new = df_new.append(row)
for _ in range(3):
df_new = df_new.append(pd.Series(), ignore_index=True)
print(df_new)
RE: empty row in pandas dataframe - snippsat - Jun-22-2021
Something like this.
import pandas as pd
items = pd.DataFrame([
["Item Type","Item Name","Price AED"],
["laptops", "HP", 2400],
["laptops", "DELL", 3400],
["laptops", "ACER", 1400]
])>>> new_row = {0: '', 1: '', 2: ''}
>>> df = items.append(new_row, ignore_index=True)
>>> df
0 1 2
0 Item Type Item Name Price AED
1 laptops HP 2400
2 laptops DELL 3400
3 laptops ACER 1400
4
RE: empty row in pandas dataframe - rwahdan - Jun-22-2021
(Jun-22-2021, 10:44 AM)snippsat Wrote: Something like this.
import pandas as pd items = pd.DataFrame([ ["Item Type","Item Name","Price AED"], ["laptops", "HP", 2400], ["laptops", "DELL", 3400], ["laptops", "ACER", 1400] ])>>> new_row = {0: '', 1: '', 2: ''} >>> df = items.append(new_row, ignore_index=True) >>> df 0 1 2 0 Item Type Item Name Price AED 1 laptops HP 2400 2 laptops DELL 3400 3 laptops ACER 1400 4
Thanks,
Is it possible not to show the row and column headings (0,1,2,3)?
RE: empty row in pandas dataframe - snippsat - Jun-22-2021
>>> items
0 1 2
0 Item Type Item Name Price AED
1 laptops HP 2400
2 laptops DELL 3400
3 laptops ACER 1400
>>>
>>> df = items.rename(columns=items.iloc[0]).drop(items.index[0])
>>> df
Item Type Item Name Price AED
1 laptops HP 2400
2 laptops DELL 3400
3 laptops ACER 1400
>>> df = df.append(pd.Series(), ignore_index=True)
>>> df
Item Type Item Name Price AED
0 laptops HP 2400
1 laptops DELL 3400
2 laptops ACER 1400
3 NaN NaN NaN
>>>
>>> print(df.to_string(index=False))
Item Type Item Name Price AED
laptops HP 2400
laptops DELL 3400
laptops ACER 1400
NaN NaN NaN