How to Sort a Pandas Dataframe Object By a Column in Python

In this article, we show how to sort a pandas dataframe object by a certain column.

Let's say you have a pandas dataframe object and they have the columns: total_bill, tip, sex, smoker, day, time, and size.

Let's say that you want to sort the dataframe by the total_bill.

This can be done with the pandas sort_values() function.

Inside of this sort_values() function, you place the name of the column that you want to sort the pandas dataframe object according to.

So, as an example, I will use the tips pandas dataframe object. This contains the columns: total_bill, tip, sex, smoker, day, time, and size.

To do this, the following code is used shown below.

import seaborn as sns df= sns.load_dataset('tips') df.sort_values(['total_bill']) total_bill tip sex smoker day time size 67 3.07 1.00 Female Yes Sat Dinner 1 92 5.75 1.00 Female Yes Fri Dinner 2 111 7.25 1.00 Female No Sat Dinner 1 172 7.25 5.15 Male Yes Sun Dinner 2 149 7.51 2.00 Male No Thur Lunch 2 195 7.56 1.44 Male No Thur Lunch 2 218 7.74 1.44 Male Yes Sat Dinner 2 145 8.35 1.50 Female No Thur Lunch 2 135 8.51 1.25 Female No Thur Lunch 2 126 8.52 1.48 Male No Thur Lunch 2 222 8.58 1.92 Male Yes Fri Lunch 1 6 8.77 2.00 Male No Sun Dinner 2 30 9.55 1.45 Male No Sat Dinner 2 178 9.60 4.00 Female Yes Sun Dinner 2 43 9.68 1.32 Male No Sun Dinner 2 148 9.78 1.73 Male No Thur Lunch 2 53 9.94 1.56 Male No Sun Dinner 2 235 10.07 1.25 Male No Sat Dinner 2 82 10.07 1.83 Female No Thur Lunch 1 226 10.09 2.00 Female Yes Fri Lunch 2 10 10.27 1.71 Male No Sun Dinner 2 51 10.29 2.60 Female No Sun Dinner 2 16 10.33 1.67 Female No Sun Dinner 3 136 10.33 2.00 Female No Thur Lunch 2 1 10.34 1.66 Male No Sun Dinner 3 196 10.34 2.00 Male Yes Thur Lunch 2 75 10.51 1.25 Male No Sat Dinner 2 168 10.59 1.61 Female Yes Sat Dinner 2 169 10.63 2.00 Female Yes Sat Dinner 2 117 10.65 1.50 Female No Thur Lunch 2 .. ... ... ... ... ... ... ... 44 30.40 5.60 Male No Sun Dinner 4 187 30.46 2.00 Male Yes Sun Dinner 5 39 31.27 5.00 Male No Sat Dinner 3 167 31.71 4.50 Male No Sun Dinner 4 173 31.85 3.18 Male Yes Sun Dinner 2 47 32.40 6.00 Male No Sun Dinner 4 83 32.68 5.00 Male Yes Thur Lunch 2 237 32.83 1.17 Male Yes Sat Dinner 2 175 32.90 3.11 Male Yes Sun Dinner 2 141 34.30 6.70 Male No Thur Lunch 6 179 34.63 3.55 Male Yes Sun Dinner 2 180 34.65 3.68 Male Yes Sun Dinner 4 52 34.81 5.20 Female No Sun Dinner 4 85 34.83 5.17 Female No Thur Lunch 4 11 35.26 5.00 Female No Sun Dinner 4 238 35.83 4.67 Female No Sat Dinner 3 56 38.01 3.00 Male Yes Sat Dinner 4 112 38.07 4.00 Male No Sun Dinner 3 207 38.73 3.00 Male Yes Sat Dinner 4 23 39.42 7.58 Male No Sat Dinner 4 95 40.17 4.73 Male Yes Fri Dinner 4 184 40.55 3.00 Male Yes Sun Dinner 2 142 41.19 5.00 Male No Thur Lunch 5 197 43.11 5.00 Female Yes Thur Lunch 4 102 44.30 2.50 Female Yes Sat Dinner 3 182 45.35 3.50 Male Yes Sun Dinner 3 156 48.17 5.00 Male No Sun Dinner 6 59 48.27 6.73 Male No Sat Dinner 4 212 48.33 9.00 Male No Sat Dinner 4 170 50.81 10.00 Male Yes Sat Dinner 3 [244 rows x 7 columns]

So let's go over the code now.

So the first thing is we import seaborn, because we want to use the tips datasheet that seaborn provides.

We then load in the tips data using the sns.load_dataset('tips'). We set this equal to the variable, df, standing for dataframe.

We then take this df variable and we use the sort_values() function in order to sort the dataframe object according to the total_bill column. You can see that the dataframe object is now sorted by the total_bill column. The values for this are in order for least to greatest. You can see that the lowest total_bill is 3.07 and the highest is 50.81.

As another example, we will sort the dataframe object now according to the tip column.

This is shown in the following code below.

import seaborn as sns df= sns.load_dataset('tips') df.sort_values(['tip']) total_bill tip sex smoker day time size 67 3.07 1.00 Female Yes Sat Dinner 1 236 12.60 1.00 Male Yes Sat Dinner 2 92 5.75 1.00 Female Yes Fri Dinner 2 111 7.25 1.00 Female No Sat Dinner 1 0 16.99 1.01 Female No Sun Dinner 2 215 12.90 1.10 Female Yes Sat Dinner 2 237 32.83 1.17 Male Yes Sat Dinner 2 235 10.07 1.25 Male No Sat Dinner 2 75 10.51 1.25 Male No Sat Dinner 2 135 8.51 1.25 Female No Thur Lunch 2 43 9.68 1.32 Male No Sun Dinner 2 146 18.64 1.36 Female No Thur Lunch 3 218 7.74 1.44 Male Yes Sat Dinner 2 195 7.56 1.44 Male No Thur Lunch 2 30 9.55 1.45 Male No Sat Dinner 2 233 10.77 1.47 Male No Sat Dinner 2 126 8.52 1.48 Male No Thur Lunch 2 190 15.69 1.50 Male Yes Sun Dinner 2 57 26.41 1.50 Female No Sat Dinner 2 97 12.03 1.50 Male Yes Fri Dinner 2 99 12.46 1.50 Male No Fri Dinner 2 117 10.65 1.50 Female No Thur Lunch 2 132 11.17 1.50 Female No Thur Lunch 2 217 11.59 1.50 Male Yes Sat Dinner 2 145 8.35 1.50 Female No Thur Lunch 2 130 19.08 1.50 Male No Thur Lunch 2 53 9.94 1.56 Male No Sun Dinner 2 12 15.42 1.57 Male No Sun Dinner 2 224 13.42 1.58 Male Yes Fri Lunch 2 168 10.59 1.61 Female Yes Sat Dinner 2 .. ... ... ... ... ... ... ... 5 25.29 4.71 Male No Sun Dinner 4 95 40.17 4.73 Male Yes Fri Dinner 4 46 22.23 5.00 Male No Sun Dinner 2 39 31.27 5.00 Male No Sat Dinner 3 142 41.19 5.00 Male No Thur Lunch 5 143 27.05 5.00 Female No Thur Lunch 6 156 48.17 5.00 Male No Sun Dinner 6 185 20.69 5.00 Male No Sun Dinner 5 11 35.26 5.00 Female No Sun Dinner 4 83 32.68 5.00 Male Yes Thur Lunch 2 197 43.11 5.00 Female Yes Thur Lunch 4 73 25.28 5.00 Female Yes Sat Dinner 2 116 29.93 5.07 Male No Sun Dinner 4 155 29.85 5.14 Female No Sun Dinner 5 172 7.25 5.15 Male Yes Sun Dinner 2 211 25.89 5.16 Male Yes Sat Dinner 4 85 34.83 5.17 Female No Thur Lunch 4 52 34.81 5.20 Female No Sun Dinner 4 44 30.40 5.60 Male No Sun Dinner 4 181 23.33 5.65 Male Yes Sun Dinner 2 88 24.71 5.85 Male No Thur Lunch 2 239 29.03 5.92 Male No Sat Dinner 3 47 32.40 6.00 Male No Sun Dinner 4 183 23.17 6.50 Male Yes Sun Dinner 4 214 28.17 6.50 Female Yes Sat Dinner 3 141 34.30 6.70 Male No Thur Lunch 6 59 48.27 6.73 Male No Sat Dinner 4 23 39.42 7.58 Male No Sat Dinner 4 212 48.33 9.00 Male No Sat Dinner 4 170 50.81 10.00 Male Yes Sat Dinner 3 [244 rows x 7 columns]

So we've now sorted the pandas dataframe object according to the tip column.

So the sort_values() function is a powerful function in pandas that allows us to sort a pandas dataframe object according to any column of a dataframe in Python.

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