Python Pandas: How to display full Dataframe i.e. print all rows & columns without truncation

In this tutorial, we will discuss the different methods to display full Dataframe i.e. print all rows & columns without truncation. So, get into this page and learn completely about Pandas dataframe in python i.e. how to print all rows & columns without truncation. Also, you can get a clear idea of how to display full dataframe from here. Pandas will be displayed column in the full dataframe.

Display Full Contents of a Dataframe
How to print an entire Pandas DataFrame in Python?
Four Methods to Print the entire pandas Dataframe
Use to_string() Method
Use pd.option_context() Method
Use pd.set_options() Method
Use pd.to_markdown() Method

Display Full Contents of a Dataframe

Pandas implement an operating system to customize the behavior & display similar stuff. By applying this benefits module we can configure the display to show the complete dataframe rather than a truncated one. A function set_option()is provided in pandas to set this kind of option,

pandas.set_option(pat, value)

It sets the value of the defined option. Let’s use this to display the full contents of a dataframe.

Setting to display All rows of Dataframe

In pandas when we print a dataframe, it displays at max_rows number of rows. If we have more rows, then it truncates the rows.

pandas.options.display.max_rows

This option outlines the maximum number of rows that pandas will present while printing a dataframe. The default value of max_rows is 10.

In case, it is set to ‘None‘ then it implies unlimited i.e. pandas will display all the rows in the dataframe. Let’s set it to None while printing the contents of above-created dataframe empDfObj,

# Default value of display.max_rows is 10 i.e. at max 10 rows will be printed.
# Set it None to display all rows in the dataframe
pd.set_option('display.max_rows', None)

Let’s examine the contents of the dataframe again,

print(empDfObj)

Output:

    A B ... Z AA
0 jack 34 ... 122 111
1 Riti 31 ... 222 211
2 Aadi 16 ... 322 311
3 Sunil 41 ... 422 411
4 Veena 33 ... 522 511
5 Shaunak 35 ... 622 611
6 Shaun 35 ... 722 711
7 jack 34 ... 122 111
8 Riti 31 ... 222 211
9 Aadi 16 ... 322 311
10 Sunil 41 ... 422 411
11 Veena 33 ... 522 511
12 Shaunak 35 ... 622 611
13 Shaun 35 ... 722 711
14 jack 34 ... 122 111
15 Riti 31 ... 222 211
16 Aadi 16 ... 322 311
17 Sunil 41 ... 422 411
18 Veena 33 ... 522 511
19 Shaunak 35 ... 622 611
20 Shaun 35 ... 722 711
21 jack 34 ... 122 111
22 Riti 31 ... 222 211
23 Aadi 16 ... 322 311
24 Sunil 41 ... 422 411
25 Veena 33 ... 522 511
26 Shaunak 35 ... 622 611
27 Shaun 35 ... 722 711
28 jack 34 ... 122 111
29 Riti 31 ... 222 211
30 Aadi 16 ... 322 311
31 Sunil 41 ... 422 411
32 Veena 33 ... 522 511
33 Shaunak 35 ... 622 611
34 Shaun 35 ... 722 711
35 jack 34 ... 122 111
36 Riti 31 ... 222 211
37 Aadi 16 ... 322 311
38 Sunil 41 ... 422 411
39 Veena 33 ... 522 511
40 Shaunak 35 ... 622 611
41 Shaun 35 ... 722 711
42 jack 34 ... 122 111
43 Riti 31 ... 222 211
44 Aadi 16 ... 322 311
45 Sunil 41 ... 422 411
46 Veena 33 ... 522 511
47 Shaunak 35 ... 622 611
48 Shaun 35 ... 722 711
49 jack 34 ... 122 111
50 Riti 31 ... 222 211
51 Aadi 16 ... 322 311
52 Sunil 41 ... 422 411
53 Veena 33 ... 522 511
54 Shaunak 35 ... 622 611
55 Shaun 35 ... 722 711
56 jack 34 ... 122 111
57 Riti 31 ... 222 211
58 Aadi 16 ... 322 311
59 Sunil 41 ... 422 411
60 Veena 33 ... 522 511
61 Shaunak 35 ... 622 611
62 Shaun 35 ... 722 711

[63 rows x 27 columns]

Also Check:

How to print an entire Pandas DataFrame in Python?

When we use a print large number of a dataset then it truncates. In this article, we are going to see how to print the entire pandas Dataframe or Series without Truncation.

The complete data frame is not printed when the length exceeds.

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
  
# Loading irirs dataset
data = load_iris()
df = pd.DataFrame(data.data,columns = data.feature_names)
print(df)

Output:

By default our complete contents of out dataframe are not printed, output got truncated. It printed only 10 rows all the remaining data is truncated. Now, what if we want to print the full dataframe without any truncation.

Four Methods to Print the entire pandas Dataframe

Use to_string() Method
Use pd.option_context() Method
Use pd.set_options() Method
Use pd.to_markdown() Method

1. Using to_string()

This is a very simple method. That is why it is not used for large files because it converts the entire data frame into a string object. But this works very well for data frames for size in the order of thousands.

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
  
data = load_iris()
df = pd.DataFrame(data.data,
                  columns = data.feature_names)
  
# Convert the whole dataframe as a string and display
print(df.to_string())

Output:

So in the above example, you have seen it printed all columns without any truncation.

2. Using pd.option_context()

option_context() and set_option() both methods are identical but there is only one difference that is one changes the settings and the other do it only within the context manager scope.

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
  
data = load_iris()
df = pd.DataFrame(data.data, 
                  columns = data.feature_names)
  
with pd.option_context('display.max_rows', None,'display.max_columns', None,
    'display.precision', 3,
                       ):
print(df)

Output:

In the above example, we are used ‘display.max_rows‘ but by default its value is 10 & if the dataframe has more rows it will truncate. So it will not be truncated we used None so all the rows are displayed.

3. Using pd.set_option()

This method is similar to pd.option_context() method and takes the same parameters. pd.reset_option(‘all’) used to reset all the changes.

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
  
data = load_iris()
df = pd.DataFrame(data.data,
                  columns = data.feature_names)
  
# Permanently changes the pandas settings
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)
  
# All dataframes hereafter reflect these changes.
print(df)
  
print('**RESET_OPTIONS**')
  
# Resets the options
pd.reset_option('all')
print(df)

Output:

**RESET_OPTIONS**

: boolean
use_inf_as_null had been deprecated and will be removed in a future
version. Use `use_inf_as_na` instead.

4. Using to_markdown()

This method is similar to the to_string() method as it also converts the data frame to a string object and also adds styling & formatting to it.

import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
  
data = load_iris()
df = pd.DataFrame(data.data,
                  columns=data.feature_names)
  
# Converts the dataframe into str object with fromatting
print(df.to_markdown())

Output:

Want to expert in the python programming language? Exploring Python Data Analysis using Pandas tutorial changes your knowledge from basic to advance level in python concepts.

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