Reputation: 9937
I want to delete all the rows in a dataframe.
The reason I want to do this is so that I can reconstruct the dataframe with an iterative loop. I want to start with a completely empty dataframe.
Alternatively, I could create an empty df from just the column / type information if that is possible
Upvotes: 54
Views: 91544
Reputation: 63
Old Thread. But i found another way
df_final=df_dup[0:0].copy(deep=True)
Upvotes: 0
Reputation: 97
df.drop(df.index,inplace=True)
This line will delete all rows, while keeping the column names.
Upvotes: 6
Reputation: 579
If you have an existing DataFrame with the columns you want then extract the column names into a list comprehension then create an empty DataFrame with your column names.
# Creating DataFrame from a CSV file with desired headers
csv_a = "path/to/my.csv"
df_a = pd.read_csv(csv_a)
# Extract column names into a list
names = [x for x in df_a.columns]
# Create empty DataFrame with those column names
df_b = pd.DataFrame(columns=names)
Upvotes: 6
Reputation: 2978
Here's another method if you have an existing DataFrame that you'd like to empty without recreating the column information:
df_empty = df[0:0]
df_empty
is a DataFrame with zero rows but with the same column structure as df
Upvotes: 118
Reputation: 16528
The latter is possible and strongly recommended - "inserting" rows row-by-row is highly inefficient. A sketch could be
>>> import numpy as np
>>> import pandas as pd
>>> index = np.arange(0, 10)
>>> df = pd.DataFrame(index=index, columns=['foo', 'bar'])
>>> df
Out[268]:
foo bar
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
6 NaN NaN
7 NaN NaN
8 NaN NaN
9 NaN NaN
Upvotes: 7