python pandas select both head and tail

You can use df.head(5) and df.tail(5) to get first five and last five.
Optionally you can create new data frame and append() head and tail:

new_df = df.tail(5)
new_df = new_df.append(df.head(5))

Small simple function:

def ends(df, x=5):
    return df.head(x).append(df.tail(x))

And use like so:

df = pd.DataFrame(np.random.rand(15,6))
ends(df,2)

I actually use this so much, I think it would be a great feature to add to pandas. (No features are to be added to pandas.DataFrame core API) I add it after import like so:

import pandas as pd
def ends(df, x=5):
    return df.head(x).append(df.tail(x))
setattr(pd.DataFrame,'ends',ends)

Use like so:

import numpy as np
df = pd.DataFrame(np.random.rand(15,6))
df.ends(2)

Associated with Linas Fx.

Defining below

pd.DataFrame.less = lambda df, n=10: df.head(n//2).append(df.tail(n//2))

then you can type only df.less()

It’s same as type df.head().append(df.tail())

If you type df.less(2), the result is same as df.head(1).append(df.tail(1))

Not quite the same question but if you just want to show the top / bottom 5 rows (eg with display in jupyter or regular print, there’s potentially a simpler way than this if you use the pd.option_context context.

#make 100 3d random numbers
df = pd.DataFrame(np.random.randn(100,3))

# sort them by their axis sum
df = df.loc[df.sum(axis=1).index]

with pd.option_context('display.max_rows',10):
    print(df)

Outputs:

           0         1         2
0  -0.649105 -0.413335  0.374872
1   3.390490  0.552708 -1.723864
2  -0.781308 -0.277342 -0.903127
3   0.433665 -1.125215 -0.290228
4  -2.028750 -0.083870 -0.094274
..       ...       ...       ...
95  0.443618 -1.473138  1.132161
96 -1.370215 -0.196425 -0.528401
97  1.062717 -0.997204 -1.666953
98  1.303512  0.699318 -0.863577
99 -0.109340 -1.330882 -1.455040

[100 rows x 3 columns]

In Jupyter, expanding on @bolster’s answer, we’ll create a reusable convenience function:

def display_n(df,n): 
    with pd.option_context('display.max_rows',n*2):
        display(df)

Then

display_n(df,2)

Returns

         0           1           2
0        0.167961    -0.732745   0.952637
1        -0.050742   -0.421239   0.444715
...      ...         ...         ...
98       0.085264    0.982093    -0.509356
99       -0.758963   -0.578267   -0.115865

(except as a nicely formatted HTML table)

when df is df = pd.DataFrame(np.random.randn(100,3))

Notes:

1. Of course you could make the same thing print as text by modifying display to print above.
2. On unix-like systems, you can the autoload the above function in all notebooks by placing it in a py or ipy file in ~/.ipython/profile_default/startup as described here.

You should use both head() and tail() for this purpose. I think the easiest way to do this is:

df.head(5).append(df.tail(5))

If you want to keep it to just Pandas, you can use apply() to concatenate the head and tail:

import pandas as pd
from string import ascii_lowercase, ascii_uppercase

df = pd.DataFrame(
    {"upper": list(ascii_uppercase), "lower": list(ascii_lowercase)}, index=range(1, 27)
)

df.apply(lambda x: pd.concat([x.head(2), x.tail(2)]))


   upper lower
1      A     a
2      B     b
25     Y     y
26     Z     z

Combining @ic_fl2 and @watsonic to give the below in Jupyter:

def ends_attr():
    def display_n(df,n):
        with pd.option_context('display.max_rows',n*2):
            display(df)
    # set pd.DataFrame attribute where .ends runs display_n() function
    setattr(pd.DataFrame,'ends',display_n)

ends_attr()

View first and last 3 rows of your df:

your_df.ends(3)

I like this because I can copy a single function and know I have everything I need to use the ends attribute.