﻿ Pandas styling: Write a Pandas program to highlight dataframe’s specific columns with different colors - w3resource

# Pandas styling Exercises: Write a Pandas program to highlight dataframe’s specific columns with different colors

## Pandas styling: Exercise-8 with Solution

Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight dataframe’s specific columns with different colors.

Sample Solution :

Python Code :

``````import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
axis=1)
df.iloc[0, 2] = np.nan
df.iloc[3, 3] = np.nan
df.iloc[4, 1] = np.nan
df.iloc[9, 4] = np.nan
print("Original array:")
print(df)
print("\nDifferent background color:")
coldict = {'B':'red', 'D':'yellow'}

def highlight_cols(x):
#copy df to new - original data are not changed
df = x.copy()
#select all values to default value - red color
df.loc[:,:] = 'background-color: red'
#overwrite values grey color
df[['B','C', 'E']] = 'background-color: grey'
#return color df
return df

df.style.apply(highlight_cols, axis=None)
``````

Original array:

```Original array:
A         B         C         D         E
0   1.0  1.329212       NaN -0.316280 -0.990810
1   2.0 -1.070816 -1.438713  0.564417  0.295722
2   3.0 -1.626404  0.219565  0.678805  1.889273
3   4.0  0.961538  0.104011       NaN  0.850229
4   5.0       NaN  1.057737  0.165562  0.515018
5   6.0 -1.336936  0.562861  1.392855 -0.063328
6   7.0  0.121668  1.207603 -0.002040  1.627796
7   8.0  0.354493  1.037528 -0.385684  0.519818
8   9.0  1.686583 -1.325963  1.428984 -2.089354
9  10.0 -0.129820  0.631523 -0.586538       NaN

Different background color:
```

Sample Output:

```
```

Python Code Editor:

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## Python: Tips of the Day

Returns the transpose of a two-dimensional list

Example:

```def tips_transpose(lst):
return list(zip(*lst))

print(tips_transpose([[2, 4, 6], [1, 3, 5], [8, 10, 12], [7, 9, 11]]))
```

Output:

```[(2, 1, 8, 7), (4, 3, 10, 9), (6, 5, 12, 11)]
```