﻿ Pandas: Total number of missing values in a DataFrame - w3resource

# Pandas: Total number of missing values in a DataFrame

## Pandas Handling Missing Values: Exercise-11 with Solution

Write a Pandas program to calculate the total number of missing values in a DataFrame.

Test Data:

```     ord_no  purch_amt    ord_date  customer_id
0       NaN        NaN         NaN          NaN
1       NaN     270.65  2012-09-10       3001.0
2   70002.0      65.26         NaN       3001.0
3       NaN        NaN         NaN          NaN
4       NaN     948.50  2012-09-10       3002.0
5   70005.0    2400.60  2012-07-27       3001.0
6       NaN    5760.00  2012-09-10       3001.0
7   70010.0    1983.43  2012-10-10       3004.0
8   70003.0    2480.40  2012-10-10       3003.0
9   70012.0     250.45  2012-06-27       3002.0
10      NaN      75.29  2012-08-17       3001.0
11      NaN        NaN         NaN          NaN
```

Sample Solution:

Python Code :

``````import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[np.nan,np.nan,70002,np.nan,np.nan,70005,np.nan,70010,70003,70012,np.nan,np.nan],
'purch_amt':[np.nan,270.65,65.26,np.nan,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,np.nan],
'ord_date': [np.nan,'2012-09-10',np.nan,np.nan,'2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17',np.nan],
'customer_id':[np.nan,3001,3001,np.nan,3002,3001,3001,3004,3003,3002,3001,np.nan]})
print("Original Orders DataFrame:")
print(df)
print("\nTotal number of missing values of the said DataFrame:")
result = df.isna().sum().sum()
print(result)
``````

Sample Output:

```Original Orders DataFrame:
ord_no  purch_amt    ord_date  customer_id
0       NaN        NaN         NaN          NaN
1       NaN     270.65  2012-09-10       3001.0
2   70002.0      65.26         NaN       3001.0
3       NaN        NaN         NaN          NaN
4       NaN     948.50  2012-09-10       3002.0
5   70005.0    2400.60  2012-07-27       3001.0
6       NaN    5760.00  2012-09-10       3001.0
7   70010.0    1983.43  2012-10-10       3004.0
8   70003.0    2480.40  2012-10-10       3003.0
9   70012.0     250.45  2012-06-27       3002.0
10      NaN      75.29  2012-08-17       3001.0
11      NaN        NaN         NaN          NaN

Total number of missing values of the said DataFrame:
17
```

Python Code Editor:

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## Python: Tips of the Day

F strings:

It is a common practice to add variables inside strings. F strings are by far the coolest way of doing it. To appreciate the f strings more, let's first perform the operation with the format function.

```name = 'Owen'
age = 25
print("{} is {} years old".format(name, age))
```

Output:

```Owen is 25 years old
```

We specify the variables that go inside the curly braces by using the format function at the end. F strings allow for specifying the variables inside the string.

```name = 'Owen'
age = 25
print(f"{name} is {age} years old")
```

Output:

```Owen is 25 years old
```

F strings are easier to follow and type. Moreover, they make the code more readable.

```A, B, C = {2, 4, 6}
print(A, B, C)
A, B, C = ['p', 'q', 'r']
print(A, B, C)
```

Output:

```2 4 6
p q r
```