PANDAS .... (Analysing)
# Analyzing Data Framesprint(df.info())
"""
Int64Index: 169 entries, 0 to 168
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Duration 169 non-null int64
1 Pulse 169 non-null int64
2 Maxpulse 169 non-null int64
3 Calories 164 non-null float64
dtypes: float64(1), int64(3)
memory usage: 6.6 KB
None
"""The info() method also tells
- Non-Null values there are present in each column, (164 of 169 Non-Null values in the "Calories" column.) .
- 5 rows with no value at all, in the "Calories" column, for whatever reason.
- Empty values, or Null values, can be bad when analyzing data, duplicate values and
- Removing rows with empty values called cleaning data, and you will learn more about that in the next chapters.
Let us dive in to the code for cleaning. Assume the data file as dirtydata.csv
import pandas as pd
df = pd.read_csv('dirtydata.csv') # reading dirty
# print(df)
"""
Duration Date Pulse Maxpulse Calories
0 60 '2020/12/01' 110 130 409.1
1 60 '2020/12/02' 117 145 479.0
2 60 '2020/12/03' 103 135 340.0
3 45 '2020/12/04' 109 175 282.4
4 45 '2020/12/05' 117 148 406.0
5 60 '2020/12/06' 102 127 300.0
6 60 '2020/12/07' 110 136 374.0
7 450 '2020/12/08' 104 134 253.3
8 30 '2020/12/09' 109 133 195.1
9 60 '2020/12/10' 98 124 269.0
10 60 '2020/12/11' 103 147 329.3
11 60 '2020/12/12' 100 120 250.7
12 60 '2020/12/12' 100 120 250.7
13 60 '2020/12/13' 106 128 345.3
14 60 '2020/12/14' 104 132 379.3
15 60 '2020/12/15' 98 123 275.0
16 60 '2020/12/16' 98 120 215.2
17 60 '2020/12/17' 100 120 300.0
18 45 '2020/12/18' 90 112 NaN
19 60 '2020/12/19' 103 123 323.0
20 45 '2020/12/20' 97 125 243.0
21 60 '2020/12/21' 108 131 364.2
22 45 NaN 100 119 282.0
23 60 '2020/12/23' 130 101 300.0
24 45 '2020/12/24' 105 132 246.0
25 60 '2020/12/25' 102 126 334.5
26 60 20201226 100 120 250.0
27 60 '2020/12/27' 92 118 241.0
28 60 '2020/12/28' 103 132 NaN
29 60 '2020/12/29' 100 132 280.0
30 60 '2020/12/30' 102 129 380.3
31 60 '2020/12/31' 92 115 243.0
"""
print(df.info)
"""
<bound method DataFrame.info of Duration Date Pulse Maxpulse Calories
0 60 '2020/12/01' 110 130 409.1
1 60 '2020/12/02' 117 145 479.0
2 60 '2020/12/03' 103 135 340.0
3 45 '2020/12/04' 109 175 282.4
4 45 '2020/12/05' 117 148 406.0
5 60 '2020/12/06' 102 127 300.0
6 60 '2020/12/07' 110 136 374.0
7 450 '2020/12/08' 104 134 253.3
8 30 '2020/12/09' 109 133 195.1
9 60 '2020/12/10' 98 124 269.0
10 60 '2020/12/11' 103 147 329.3
11 60 '2020/12/12' 100 120 250.7
12 60 '2020/12/12' 100 120 250.7
13 60 '2020/12/13' 106 128 345.3
14 60 '2020/12/14' 104 132 379.3
15 60 '2020/12/15' 98 123 275.0
16 60 '2020/12/16' 98 120 215.2
17 60 '2020/12/17' 100 120 300.0
19 60 '2020/12/19' 103 123 323.0
20 45 '2020/12/20' 97 125 243.0
21 60 '2020/12/21' 108 131 364.2
23 60 '2020/12/23' 130 101 300.0
24 45 '2020/12/24' 105 132 246.0
25 60 '2020/12/25' 102 126 334.5
26 60 20201226 100 120 250.0
27 60 '2020/12/27' 92 118 241.0
29 60 '2020/12/29' 100 132 280.0
30 60 '2020/12/30' 102 129 380.3
31 60 '2020/12/31' 92 115 243.0>
"""
df.dropna(inplace = True)
# print(df.to_string())
print(df) # check no values are dropped
"""
Duration Date Pulse Maxpulse Calories
0 60 '2020/12/01' 110 130 409.1
1 60 '2020/12/02' 117 145 479.0
2 60 '2020/12/03' 103 135 340.0
3 45 '2020/12/04' 109 175 282.4
4 45 '2020/12/05' 117 148 406.0
5 60 '2020/12/06' 102 127 300.0
6 60 '2020/12/07' 110 136 374.0
7 450 '2020/12/08' 104 134 253.3
8 30 '2020/12/09' 109 133 195.1
9 60 '2020/12/10' 98 124 269.0
10 60 '2020/12/11' 103 147 329.3
11 60 '2020/12/12' 100 120 250.7
12 60 '2020/12/12' 100 120 250.7
13 60 '2020/12/13' 106 128 345.3
14 60 '2020/12/14' 104 132 379.3
15 60 '2020/12/15' 98 123 275.0
16 60 '2020/12/16' 98 120 215.2
17 60 '2020/12/17' 100 120 300.0
19 60 '2020/12/19' 103 123 323.0
20 45 '2020/12/20' 97 125 243.0
21 60 '2020/12/21' 108 131 364.2
23 60 '2020/12/23' 130 101 300.0
24 45 '2020/12/24' 105 132 246.0
25 60 '2020/12/25' 102 126 334.5
26 60 20201226 100 120 250.0
27 60 '2020/12/27' 92 118 241.0
29 60 '2020/12/29' 100 132 280.0
30 60 '2020/12/30' 102 129 380.3
31 60 '2020/12/31' 92 115 243.0
"""
See the last result output doesn't have NaN values. inplace = True, it will clean in the same place.
dropna() will delete the rows with NaN.
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