Python pandas insert empty rows after each row

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Python pandas insert empty rows after each row

  1. How to solve Python pandas insert empty rows after each row

    Using append on a dataframe is quite inefficient I believe (has to reallocate memory for the entire data frame each time).
    DataFrames were meant for analyzing data and easily adding columns—but not rows.
    So I think a good approach would be to create a new dataframe of the correct size and then transfer the data over to it. Easiest way to do that is using an index.
    # Demonstration data data = 'name profession Bill cashier Sam stock Adam security' data = np.array(data.split()).reshape((4,2)) df = pd.DataFrame(data[1:],columns=data[0]) # Add n blank rows n = 3 new_index = pd.RangeIndex(len(df)*(n+1)) new_df = pd.DataFrame(np.nan, index=new_index, columns=df.columns) ids = np.arange(len(df))*(n+1) new_df.loc[ids] = df.values print(new_df)
    Output:
    name profession 0 Bill cashier 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 Sam stock 5 NaN NaN 6 NaN NaN 7 NaN NaN 8 Adam security 9 NaN NaN 10 NaN NaN 11 NaN NaN

  2. Python pandas insert empty rows after each row

    Using append on a dataframe is quite inefficient I believe (has to reallocate memory for the entire data frame each time).
    DataFrames were meant for analyzing data and easily adding columns—but not rows.
    So I think a good approach would be to create a new dataframe of the correct size and then transfer the data over to it. Easiest way to do that is using an index.
    # Demonstration data data = 'name profession Bill cashier Sam stock Adam security' data = np.array(data.split()).reshape((4,2)) df = pd.DataFrame(data[1:],columns=data[0]) # Add n blank rows n = 3 new_index = pd.RangeIndex(len(df)*(n+1)) new_df = pd.DataFrame(np.nan, index=new_index, columns=df.columns) ids = np.arange(len(df))*(n+1) new_df.loc[ids] = df.values print(new_df)
    Output:
    name profession 0 Bill cashier 1 NaN NaN 2 NaN NaN 3 NaN NaN 4 Sam stock 5 NaN NaN 6 NaN NaN 7 NaN NaN 8 Adam security 9 NaN NaN 10 NaN NaN 11 NaN NaN

Solution 1

Using append on a dataframe is quite inefficient I believe (has to reallocate memory for the entire data frame each time).

DataFrames were meant for analyzing data and easily adding columns—but not rows.

So I think a good approach would be to create a new dataframe of the correct size and then transfer the data over to it. Easiest way to do that is using an index.

# Demonstration data
data = 'name profession Bill cashier Sam stock Adam security'
data = np.array(data.split()).reshape((4,2))
df = pd.DataFrame(data[1:],columns=data[0])

# Add n blank rows
n = 3
new_index = pd.RangeIndex(len(df)*(n+1))
new_df = pd.DataFrame(np.nan, index=new_index, columns=df.columns)
ids = np.arange(len(df))*(n+1)
new_df.loc[ids] = df.values
print(new_df)

Output:

    name profession
0   Bill    cashier
1    NaN        NaN
2    NaN        NaN
3    NaN        NaN
4    Sam      stock
5    NaN        NaN
6    NaN        NaN
7    NaN        NaN
8   Adam   security
9    NaN        NaN
10   NaN        NaN
11   NaN        NaN

Original Author Bill Of This Content

Solution 2

The code below includes a function to add empty rows between the existing rows of a dataframe.

Might not be the best approach for what you want to do, it might be better to add the blank rows when you are exporting the data.

import pandas as pd

def add_blank_rows(df, no_rows):
    df_new = pd.DataFrame(columns=df.columns)
    for idx in range(len(df)):
        df_new = df_new.append(df.iloc[idx])
        for _ in range(no_rows):
            df_new=df_new.append(pd.Series(), ignore_index=True)
    return df_new
    
df = pd.read_csv('test.csv')

df_with_blank_rows = add_blank_rows(df, 3)

print(df_with_blank_rows)

Original Author norie Of This Content

Solution 3

 # original_df is your original dataframe
 # new_df is the resulting dataframe with empty rows
 # empty_rows is the amount of rows you want to insert (change to number of rows you need):
 
 empty_rows = 3
 original_df.index = range(0, empty_rows*len(original_df), empty_rows)
 new_df = original_df.reindex(index=range(empty_rows*len(original_df)))

Original Author PeJota Of This Content

Solution 4

If you provided more information that would be helpful, but a thing that comes to mind is to use this command

df.append(pd.Series(), ignore_index=True)

This will add an empty row to your data frame, though as you can see you have to pass set ignore_index=True, otherwise the append won’t work.

Original Author djvaroli Of This Content

Conclusion

So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.

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I am an Information Technology Engineer. I have Completed my MCA And I have 4 Year Plus Experience, I am a web developer with knowledge of multiple back-end platforms Like PHP, Node.js, Python and frontend JavaScript frameworks Like Angular, React, and Vue.

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