In this lesson we're going to dig into various methods for accessing data from our Pandas Series and DataFrames.
You will be able to:
- Understand and explain some key Pandas methods
- Access DataFrame data by using the label
- Perform boolean indexing on both Series and DataFrames
- Use simple selectors for series
- Set new Series and DataFrame inputs
First, let's make sure we import pandas as pd.
import pandas as pdTo show how to access data with Pandas, let's use the "wine" data set in the scikit-learn library (you might have heard about this library before - you'll use it extensively when we get to machine learning!). Don't worry about the code below, we're essentially just making sure you have access to the wine data set.
The data contained in the wine data set are the results of a chemical analysis of wines grown in Italy. It contains the quantities of 13 wine constituents.
from sklearn.datasets import load_wine
data = load_wine()
df = pd.DataFrame(data.data, columns=data.feature_names)Great! Our data set is now stored in the variable df. As you know, you can look at its elements by using df or print(df).
print(df) alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \
0 14.23 1.71 2.43 15.6 127.0 2.80
1 13.20 1.78 2.14 11.2 100.0 2.65
2 13.16 2.36 2.67 18.6 101.0 2.80
3 14.37 1.95 2.50 16.8 113.0 3.85
4 13.24 2.59 2.87 21.0 118.0 2.80
5 14.20 1.76 2.45 15.2 112.0 3.27
6 14.39 1.87 2.45 14.6 96.0 2.50
7 14.06 2.15 2.61 17.6 121.0 2.60
8 14.83 1.64 2.17 14.0 97.0 2.80
9 13.86 1.35 2.27 16.0 98.0 2.98
10 14.10 2.16 2.30 18.0 105.0 2.95
11 14.12 1.48 2.32 16.8 95.0 2.20
12 13.75 1.73 2.41 16.0 89.0 2.60
13 14.75 1.73 2.39 11.4 91.0 3.10
14 14.38 1.87 2.38 12.0 102.0 3.30
15 13.63 1.81 2.70 17.2 112.0 2.85
16 14.30 1.92 2.72 20.0 120.0 2.80
17 13.83 1.57 2.62 20.0 115.0 2.95
18 14.19 1.59 2.48 16.5 108.0 3.30
19 13.64 3.10 2.56 15.2 116.0 2.70
20 14.06 1.63 2.28 16.0 126.0 3.00
21 12.93 3.80 2.65 18.6 102.0 2.41
22 13.71 1.86 2.36 16.6 101.0 2.61
23 12.85 1.60 2.52 17.8 95.0 2.48
24 13.50 1.81 2.61 20.0 96.0 2.53
25 13.05 2.05 3.22 25.0 124.0 2.63
26 13.39 1.77 2.62 16.1 93.0 2.85
27 13.30 1.72 2.14 17.0 94.0 2.40
28 13.87 1.90 2.80 19.4 107.0 2.95
29 14.02 1.68 2.21 16.0 96.0 2.65
.. ... ... ... ... ... ...
148 13.32 3.24 2.38 21.5 92.0 1.93
149 13.08 3.90 2.36 21.5 113.0 1.41
150 13.50 3.12 2.62 24.0 123.0 1.40
151 12.79 2.67 2.48 22.0 112.0 1.48
152 13.11 1.90 2.75 25.5 116.0 2.20
153 13.23 3.30 2.28 18.5 98.0 1.80
154 12.58 1.29 2.10 20.0 103.0 1.48
155 13.17 5.19 2.32 22.0 93.0 1.74
156 13.84 4.12 2.38 19.5 89.0 1.80
157 12.45 3.03 2.64 27.0 97.0 1.90
158 14.34 1.68 2.70 25.0 98.0 2.80
159 13.48 1.67 2.64 22.5 89.0 2.60
160 12.36 3.83 2.38 21.0 88.0 2.30
161 13.69 3.26 2.54 20.0 107.0 1.83
162 12.85 3.27 2.58 22.0 106.0 1.65
163 12.96 3.45 2.35 18.5 106.0 1.39
164 13.78 2.76 2.30 22.0 90.0 1.35
165 13.73 4.36 2.26 22.5 88.0 1.28
166 13.45 3.70 2.60 23.0 111.0 1.70
167 12.82 3.37 2.30 19.5 88.0 1.48
168 13.58 2.58 2.69 24.5 105.0 1.55
169 13.40 4.60 2.86 25.0 112.0 1.98
170 12.20 3.03 2.32 19.0 96.0 1.25
171 12.77 2.39 2.28 19.5 86.0 1.39
172 14.16 2.51 2.48 20.0 91.0 1.68
173 13.71 5.65 2.45 20.5 95.0 1.68
174 13.40 3.91 2.48 23.0 102.0 1.80
175 13.27 4.28 2.26 20.0 120.0 1.59
176 13.17 2.59 2.37 20.0 120.0 1.65
177 14.13 4.10 2.74 24.5 96.0 2.05
flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \
0 3.06 0.28 2.29 5.640000 1.04
1 2.76 0.26 1.28 4.380000 1.05
2 3.24 0.30 2.81 5.680000 1.03
3 3.49 0.24 2.18 7.800000 0.86
4 2.69 0.39 1.82 4.320000 1.04
5 3.39 0.34 1.97 6.750000 1.05
6 2.52 0.30 1.98 5.250000 1.02
7 2.51 0.31 1.25 5.050000 1.06
8 2.98 0.29 1.98 5.200000 1.08
9 3.15 0.22 1.85 7.220000 1.01
10 3.32 0.22 2.38 5.750000 1.25
11 2.43 0.26 1.57 5.000000 1.17
12 2.76 0.29 1.81 5.600000 1.15
13 3.69 0.43 2.81 5.400000 1.25
14 3.64 0.29 2.96 7.500000 1.20
15 2.91 0.30 1.46 7.300000 1.28
16 3.14 0.33 1.97 6.200000 1.07
17 3.40 0.40 1.72 6.600000 1.13
18 3.93 0.32 1.86 8.700000 1.23
19 3.03 0.17 1.66 5.100000 0.96
20 3.17 0.24 2.10 5.650000 1.09
21 2.41 0.25 1.98 4.500000 1.03
22 2.88 0.27 1.69 3.800000 1.11
23 2.37 0.26 1.46 3.930000 1.09
24 2.61 0.28 1.66 3.520000 1.12
25 2.68 0.47 1.92 3.580000 1.13
26 2.94 0.34 1.45 4.800000 0.92
27 2.19 0.27 1.35 3.950000 1.02
28 2.97 0.37 1.76 4.500000 1.25
29 2.33 0.26 1.98 4.700000 1.04
.. ... ... ... ... ...
148 0.76 0.45 1.25 8.420000 0.55
149 1.39 0.34 1.14 9.400000 0.57
150 1.57 0.22 1.25 8.600000 0.59
151 1.36 0.24 1.26 10.800000 0.48
152 1.28 0.26 1.56 7.100000 0.61
153 0.83 0.61 1.87 10.520000 0.56
154 0.58 0.53 1.40 7.600000 0.58
155 0.63 0.61 1.55 7.900000 0.60
156 0.83 0.48 1.56 9.010000 0.57
157 0.58 0.63 1.14 7.500000 0.67
158 1.31 0.53 2.70 13.000000 0.57
159 1.10 0.52 2.29 11.750000 0.57
160 0.92 0.50 1.04 7.650000 0.56
161 0.56 0.50 0.80 5.880000 0.96
162 0.60 0.60 0.96 5.580000 0.87
163 0.70 0.40 0.94 5.280000 0.68
164 0.68 0.41 1.03 9.580000 0.70
165 0.47 0.52 1.15 6.620000 0.78
166 0.92 0.43 1.46 10.680000 0.85
167 0.66 0.40 0.97 10.260000 0.72
168 0.84 0.39 1.54 8.660000 0.74
169 0.96 0.27 1.11 8.500000 0.67
170 0.49 0.40 0.73 5.500000 0.66
171 0.51 0.48 0.64 9.899999 0.57
172 0.70 0.44 1.24 9.700000 0.62
173 0.61 0.52 1.06 7.700000 0.64
174 0.75 0.43 1.41 7.300000 0.70
175 0.69 0.43 1.35 10.200000 0.59
176 0.68 0.53 1.46 9.300000 0.60
177 0.76 0.56 1.35 9.200000 0.61
od280/od315_of_diluted_wines proline
0 3.92 1065.0
1 3.40 1050.0
2 3.17 1185.0
3 3.45 1480.0
4 2.93 735.0
5 2.85 1450.0
6 3.58 1290.0
7 3.58 1295.0
8 2.85 1045.0
9 3.55 1045.0
10 3.17 1510.0
11 2.82 1280.0
12 2.90 1320.0
13 2.73 1150.0
14 3.00 1547.0
15 2.88 1310.0
16 2.65 1280.0
17 2.57 1130.0
18 2.82 1680.0
19 3.36 845.0
20 3.71 780.0
21 3.52 770.0
22 4.00 1035.0
23 3.63 1015.0
24 3.82 845.0
25 3.20 830.0
26 3.22 1195.0
27 2.77 1285.0
28 3.40 915.0
29 3.59 1035.0
.. ... ...
148 1.62 650.0
149 1.33 550.0
150 1.30 500.0
151 1.47 480.0
152 1.33 425.0
153 1.51 675.0
154 1.55 640.0
155 1.48 725.0
156 1.64 480.0
157 1.73 880.0
158 1.96 660.0
159 1.78 620.0
160 1.58 520.0
161 1.82 680.0
162 2.11 570.0
163 1.75 675.0
164 1.68 615.0
165 1.75 520.0
166 1.56 695.0
167 1.75 685.0
168 1.80 750.0
169 1.92 630.0
170 1.83 510.0
171 1.63 470.0
172 1.71 660.0
173 1.74 740.0
174 1.56 750.0
175 1.56 835.0
176 1.62 840.0
177 1.60 560.0
[178 rows x 13 columns]
Now what if you only want to see only a few lines of the data, based on certain constraints? You'll learn how to access data in this lesson!
It won't be a surprise that our df object is a pandas DataFrame object. Let's verify this using the type()-function
type(df)pandas.core.frame.DataFrame
There are some methods and attributes associated with pandas objects (both DataFrames and series!) which makes retrieving information from the data particularly easy. Some commonly used methods:
.head().tail()
And attributes:
.index.columns.dtypes.shape
By using .head() and .tail(), you can select the first
# First 5 rows of df
df.head().dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | color_intensity | hue | od280/od315_of_diluted_wines | proline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14.23 | 1.71 | 2.43 | 15.6 | 127.0 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065.0 |
| 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100.0 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050.0 |
| 2 | 13.16 | 2.36 | 2.67 | 18.6 | 101.0 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185.0 |
| 3 | 14.37 | 1.95 | 2.50 | 16.8 | 113.0 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480.0 |
| 4 | 13.24 | 2.59 | 2.87 | 21.0 | 118.0 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735.0 |
# last 3 rows of df
df.tail(3).dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | color_intensity | hue | od280/od315_of_diluted_wines | proline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 175 | 13.27 | 4.28 | 2.26 | 20.0 | 120.0 | 1.59 | 0.69 | 0.43 | 1.35 | 10.2 | 0.59 | 1.56 | 835.0 |
| 176 | 13.17 | 2.59 | 2.37 | 20.0 | 120.0 | 1.65 | 0.68 | 0.53 | 1.46 | 9.3 | 0.60 | 1.62 | 840.0 |
| 177 | 14.13 | 4.10 | 2.74 | 24.5 | 96.0 | 2.05 | 0.76 | 0.56 | 1.35 | 9.2 | 0.61 | 1.60 | 560.0 |
To get a concise summary of the dataframe you can use .info()
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 178 entries, 0 to 177
Data columns (total 13 columns):
alcohol 178 non-null float64
malic_acid 178 non-null float64
ash 178 non-null float64
alcalinity_of_ash 178 non-null float64
magnesium 178 non-null float64
total_phenols 178 non-null float64
flavanoids 178 non-null float64
nonflavanoid_phenols 178 non-null float64
proanthocyanins 178 non-null float64
color_intensity 178 non-null float64
hue 178 non-null float64
od280/od315_of_diluted_wines 178 non-null float64
proline 178 non-null float64
dtypes: float64(13)
memory usage: 18.2 KB
Using .index you can access the index or row labels of the DataFrame.
df.indexRangeIndex(start=0, stop=178, step=1)
Using .columns, you can access the column labels of the DataFrame.
df.columnsIndex(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',
'total_phenols', 'flavanoids', 'nonflavanoid_phenols',
'proanthocyanins', 'color_intensity', 'hue',
'od280/od315_of_diluted_wines', 'proline'],
dtype='object')
Using .dtypes returns the dtypes in the DataFrame (compare with `.info()!)
df.dtypesalcohol float64
malic_acid float64
ash float64
alcalinity_of_ash float64
magnesium float64
total_phenols float64
flavanoids float64
nonflavanoid_phenols float64
proanthocyanins float64
color_intensity float64
hue float64
od280/od315_of_diluted_wines float64
proline float64
dtype: object
.shape returns a tuple representing the dimensionality (in (rows,columns) ) of the DataFrame.
df.shape(178, 13)
In the previous section, we deliberately omitted 2 very important attributes:
.iloc, which is a pandas dataframe indexer used for integer-location based indexing / selection by position..loc, which has 2 use cases: - Selecting by label / index - Selecting with a boolean / conditional lookup
You can use .iloc to select single rows. To select the 4th row, you can use .iloc[3] like:
df.iloc[3]alcohol 14.37
malic_acid 1.95
ash 2.50
alcalinity_of_ash 16.80
magnesium 113.00
total_phenols 3.85
flavanoids 3.49
nonflavanoid_phenols 0.24
proanthocyanins 2.18
color_intensity 7.80
hue 0.86
od280/od315_of_diluted_wines 3.45
proline 1480.00
Name: 3, dtype: float64
You can use a colon to select several columns. Note that you'll use a structure .iloc[a:b] where the row with index a will be included in the selection and the row with index b is excluded.
df.iloc[5:8].dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | color_intensity | hue | od280/od315_of_diluted_wines | proline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 14.20 | 1.76 | 2.45 | 15.2 | 112.0 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450.0 |
| 6 | 14.39 | 1.87 | 2.45 | 14.6 | 96.0 | 2.50 | 2.52 | 0.30 | 1.98 | 5.25 | 1.02 | 3.58 | 1290.0 |
| 7 | 14.06 | 2.15 | 2.61 | 17.6 | 121.0 | 2.60 | 2.51 | 0.31 | 1.25 | 5.05 | 1.06 | 3.58 | 1295.0 |
Next, you can use , to perform column selections based on their index as well. The command below selects full columns 3-6:
df.iloc[:,3:7].dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcalinity_of_ash | magnesium | total_phenols | flavanoids | |
|---|---|---|---|---|
| 0 | 15.6 | 127.0 | 2.80 | 3.06 |
| 1 | 11.2 | 100.0 | 2.65 | 2.76 |
| 2 | 18.6 | 101.0 | 2.80 | 3.24 |
| 3 | 16.8 | 113.0 | 3.85 | 3.49 |
| 4 | 21.0 | 118.0 | 2.80 | 2.69 |
| 5 | 15.2 | 112.0 | 3.27 | 3.39 |
| 6 | 14.6 | 96.0 | 2.50 | 2.52 |
| 7 | 17.6 | 121.0 | 2.60 | 2.51 |
| 8 | 14.0 | 97.0 | 2.80 | 2.98 |
| 9 | 16.0 | 98.0 | 2.98 | 3.15 |
| 10 | 18.0 | 105.0 | 2.95 | 3.32 |
| 11 | 16.8 | 95.0 | 2.20 | 2.43 |
| 12 | 16.0 | 89.0 | 2.60 | 2.76 |
| 13 | 11.4 | 91.0 | 3.10 | 3.69 |
| 14 | 12.0 | 102.0 | 3.30 | 3.64 |
| 15 | 17.2 | 112.0 | 2.85 | 2.91 |
| 16 | 20.0 | 120.0 | 2.80 | 3.14 |
| 17 | 20.0 | 115.0 | 2.95 | 3.40 |
| 18 | 16.5 | 108.0 | 3.30 | 3.93 |
| 19 | 15.2 | 116.0 | 2.70 | 3.03 |
| 20 | 16.0 | 126.0 | 3.00 | 3.17 |
| 21 | 18.6 | 102.0 | 2.41 | 2.41 |
| 22 | 16.6 | 101.0 | 2.61 | 2.88 |
| 23 | 17.8 | 95.0 | 2.48 | 2.37 |
| 24 | 20.0 | 96.0 | 2.53 | 2.61 |
| 25 | 25.0 | 124.0 | 2.63 | 2.68 |
| 26 | 16.1 | 93.0 | 2.85 | 2.94 |
| 27 | 17.0 | 94.0 | 2.40 | 2.19 |
| 28 | 19.4 | 107.0 | 2.95 | 2.97 |
| 29 | 16.0 | 96.0 | 2.65 | 2.33 |
| ... | ... | ... | ... | ... |
| 148 | 21.5 | 92.0 | 1.93 | 0.76 |
| 149 | 21.5 | 113.0 | 1.41 | 1.39 |
| 150 | 24.0 | 123.0 | 1.40 | 1.57 |
| 151 | 22.0 | 112.0 | 1.48 | 1.36 |
| 152 | 25.5 | 116.0 | 2.20 | 1.28 |
| 153 | 18.5 | 98.0 | 1.80 | 0.83 |
| 154 | 20.0 | 103.0 | 1.48 | 0.58 |
| 155 | 22.0 | 93.0 | 1.74 | 0.63 |
| 156 | 19.5 | 89.0 | 1.80 | 0.83 |
| 157 | 27.0 | 97.0 | 1.90 | 0.58 |
| 158 | 25.0 | 98.0 | 2.80 | 1.31 |
| 159 | 22.5 | 89.0 | 2.60 | 1.10 |
| 160 | 21.0 | 88.0 | 2.30 | 0.92 |
| 161 | 20.0 | 107.0 | 1.83 | 0.56 |
| 162 | 22.0 | 106.0 | 1.65 | 0.60 |
| 163 | 18.5 | 106.0 | 1.39 | 0.70 |
| 164 | 22.0 | 90.0 | 1.35 | 0.68 |
| 165 | 22.5 | 88.0 | 1.28 | 0.47 |
| 166 | 23.0 | 111.0 | 1.70 | 0.92 |
| 167 | 19.5 | 88.0 | 1.48 | 0.66 |
| 168 | 24.5 | 105.0 | 1.55 | 0.84 |
| 169 | 25.0 | 112.0 | 1.98 | 0.96 |
| 170 | 19.0 | 96.0 | 1.25 | 0.49 |
| 171 | 19.5 | 86.0 | 1.39 | 0.51 |
| 172 | 20.0 | 91.0 | 1.68 | 0.70 |
| 173 | 20.5 | 95.0 | 1.68 | 0.61 |
| 174 | 23.0 | 102.0 | 1.80 | 0.75 |
| 175 | 20.0 | 120.0 | 1.59 | 0.69 |
| 176 | 20.0 | 120.0 | 1.65 | 0.68 |
| 177 | 24.5 | 96.0 | 2.05 | 0.76 |
178 rows × 4 columns
Last but not least, you can perform column and row selections at once:
df.iloc[5:10,3:9].dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | |
|---|---|---|---|---|---|---|
| 5 | 15.2 | 112.0 | 3.27 | 3.39 | 0.34 | 1.97 |
| 6 | 14.6 | 96.0 | 2.50 | 2.52 | 0.30 | 1.98 |
| 7 | 17.6 | 121.0 | 2.60 | 2.51 | 0.31 | 1.25 |
| 8 | 14.0 | 97.0 | 2.80 | 2.98 | 0.29 | 1.98 |
| 9 | 16.0 | 98.0 | 2.98 | 3.15 | 0.22 | 1.85 |
You can .loc to select columns based on their (row index and) column name. Examples:
df.loc[:,"magnesium"]0 127.0
1 100.0
2 101.0
3 113.0
4 118.0
5 112.0
6 96.0
7 121.0
8 97.0
9 98.0
10 105.0
11 95.0
12 89.0
13 91.0
14 102.0
15 112.0
16 120.0
17 115.0
18 108.0
19 116.0
20 126.0
21 102.0
22 101.0
23 95.0
24 96.0
25 124.0
26 93.0
27 94.0
28 107.0
29 96.0
...
148 92.0
149 113.0
150 123.0
151 112.0
152 116.0
153 98.0
154 103.0
155 93.0
156 89.0
157 97.0
158 98.0
159 89.0
160 88.0
161 107.0
162 106.0
163 106.0
164 90.0
165 88.0
166 111.0
167 88.0
168 105.0
169 112.0
170 96.0
171 86.0
172 91.0
173 95.0
174 102.0
175 120.0
176 120.0
177 96.0
Name: magnesium, Length: 178, dtype: float64
An alternative method here is simply calling df["magnesium"]!
df.loc[7:16,"magnesium"]7 121.0
8 97.0
9 98.0
10 105.0
11 95.0
12 89.0
13 91.0
14 102.0
15 112.0
16 120.0
Name: magnesium, dtype: float64
Sometimes you'd like to select certain rows in your data set based on the value for a certain variable. Imagine you'd like to create a new dataframe that only contains the wines with an alcohol percentage below 12. This can be done as follows:
df.loc[df["alcohol"]<12].dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols | proanthocyanins | color_intensity | hue | od280/od315_of_diluted_wines | proline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 74 | 11.96 | 1.09 | 2.30 | 21.0 | 101.0 | 3.38 | 2.14 | 0.13 | 1.65 | 3.21 | 0.99 | 3.13 | 886.0 |
| 75 | 11.66 | 1.88 | 1.92 | 16.0 | 97.0 | 1.61 | 1.57 | 0.34 | 1.15 | 3.80 | 1.23 | 2.14 | 428.0 |
| 77 | 11.84 | 2.89 | 2.23 | 18.0 | 112.0 | 1.72 | 1.32 | 0.43 | 0.95 | 2.65 | 0.96 | 2.52 | 500.0 |
| 84 | 11.84 | 0.89 | 2.58 | 18.0 | 94.0 | 2.20 | 2.21 | 0.22 | 2.35 | 3.05 | 0.79 | 3.08 | 520.0 |
| 87 | 11.65 | 1.67 | 2.62 | 26.0 | 88.0 | 1.92 | 1.61 | 0.40 | 1.34 | 2.60 | 1.36 | 3.21 | 562.0 |
| 88 | 11.64 | 2.06 | 2.46 | 21.6 | 84.0 | 1.95 | 1.69 | 0.48 | 1.35 | 2.80 | 1.00 | 2.75 | 680.0 |
| 94 | 11.62 | 1.99 | 2.28 | 18.0 | 98.0 | 3.02 | 2.26 | 0.17 | 1.35 | 3.25 | 1.16 | 2.96 | 345.0 |
| 96 | 11.81 | 2.12 | 2.74 | 21.5 | 134.0 | 1.60 | 0.99 | 0.14 | 1.56 | 2.50 | 0.95 | 2.26 | 625.0 |
| 103 | 11.82 | 1.72 | 1.88 | 19.5 | 86.0 | 2.50 | 1.64 | 0.37 | 1.42 | 2.06 | 0.94 | 2.44 | 415.0 |
| 109 | 11.61 | 1.35 | 2.70 | 20.0 | 94.0 | 2.74 | 2.92 | 0.29 | 2.49 | 2.65 | 0.96 | 3.26 | 680.0 |
| 110 | 11.46 | 3.74 | 1.82 | 19.5 | 107.0 | 3.18 | 2.58 | 0.24 | 3.58 | 2.90 | 0.75 | 2.81 | 562.0 |
| 112 | 11.76 | 2.68 | 2.92 | 20.0 | 103.0 | 1.75 | 2.03 | 0.60 | 1.05 | 3.80 | 1.23 | 2.50 | 607.0 |
| 113 | 11.41 | 0.74 | 2.50 | 21.0 | 88.0 | 2.48 | 2.01 | 0.42 | 1.44 | 3.08 | 1.10 | 2.31 | 434.0 |
| 115 | 11.03 | 1.51 | 2.20 | 21.5 | 85.0 | 2.46 | 2.17 | 0.52 | 2.01 | 1.90 | 1.71 | 2.87 | 407.0 |
| 116 | 11.82 | 1.47 | 1.99 | 20.8 | 86.0 | 1.98 | 1.60 | 0.30 | 1.53 | 1.95 | 0.95 | 3.33 | 495.0 |
| 120 | 11.45 | 2.40 | 2.42 | 20.0 | 96.0 | 2.90 | 2.79 | 0.32 | 1.83 | 3.25 | 0.80 | 3.39 | 625.0 |
| 121 | 11.56 | 2.05 | 3.23 | 28.5 | 119.0 | 3.18 | 5.08 | 0.47 | 1.87 | 6.00 | 0.93 | 3.69 | 465.0 |
| 124 | 11.87 | 4.31 | 2.39 | 21.0 | 82.0 | 2.86 | 3.03 | 0.21 | 2.91 | 2.80 | 0.75 | 3.64 | 380.0 |
| 127 | 11.79 | 2.13 | 2.78 | 28.5 | 92.0 | 2.13 | 2.24 | 0.58 | 1.76 | 3.00 | 0.97 | 2.44 | 466.0 |
You can verify that simply using df[df["alcohol"]<12], you can obtain the same result!
However, the .loc attribute is useful if you'd only want the color intensity for the wines with an alcohol percentage below 12. You can obtain the result as follows:
df.loc[df["alcohol"]<12, ["color_intensity"]].dataframe tbody tr th {
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}
.dataframe thead th {
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| color_intensity | |
|---|---|
| 74 | 3.21 |
| 75 | 3.80 |
| 77 | 2.65 |
| 84 | 3.05 |
| 87 | 2.60 |
| 88 | 2.80 |
| 94 | 3.25 |
| 96 | 2.50 |
| 103 | 2.06 |
| 109 | 2.65 |
| 110 | 2.90 |
| 112 | 3.80 |
| 113 | 3.08 |
| 115 | 1.90 |
| 116 | 1.95 |
| 120 | 3.25 |
| 121 | 6.00 |
| 124 | 2.80 |
| 127 | 3.00 |
Until now we've only really discussed pandas DataFrames. Most of these methods and selectors are also applicable to pandas series. See how you can convert a one-column DataFrame into a Pandas Series:
# Let's save our color intensity dataframe into an object `col_intensity`
col_intensity = df["color_intensity"]type(col_intensity)pandas.core.series.Series
Note how col_intensity is now a pandas Series.
Many of the commands discussed before are readily applicable to series:
col_intensity[0:3]0 5.64
1 4.38
2 5.68
Name: color_intensity, dtype: float64
col_intensity[col_intensity > 8] # or col_intensity.loc[col_intensity>8]18 8.700000
49 8.900000
144 8.210000
148 8.420000
149 9.400000
150 8.600000
151 10.800000
153 10.520000
156 9.010000
158 13.000000
159 11.750000
164 9.580000
166 10.680000
167 10.260000
168 8.660000
169 8.500000
171 9.899999
172 9.700000
175 10.200000
176 9.300000
177 9.200000
Name: color_intensity, dtype: float64
Imagine that for some reason, you're not interested in the color intensity values for color intensities above 10, and simply want to set all color intensities to 10 when they are bigger than 10. You can use a selector method and then assign it a new value, just like this:
df.loc[df["color_intensity"]>10, "color_intensity"] = 10Now imagine that we want to create a new column named "shade" which has a value "light" when the color_intensity is below 7, and "dark" when the intensity is > 7. This can be done as follows:
df.loc[df["color_intensity"]>7, "shade"] = "dark"
df.loc[df["color_intensity"]<=7, "shade"] = "light"Have another look at df. shade is added as a 14th column!
We've introduded a range of techniques for accessing information in Pandas Series and DataFrames, selecting rows and columns, changing values, and creating new columns! Now, it's time for some practice! Let's start working on a lab where you will get a chance to combine some of these methods!