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lines changed Original file line number Diff line number Diff line change 22 "cells" : [
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44 "cell_type" : " markdown"
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88 " # Exercise: exploring a new table\n "
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3232 " Now use the skrub `TableReport` and answer the following questions:"
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8282 " ## Answers\n "
104104 },
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108108 "metadata" : {},
109109 "source" : [
110110 " # Exercise: clean a dataframe using the `Cleaner`\n "
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131131 " Use the `TableReport` to answer the following questions:\n "
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139139 "cell_type" : " code"
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149149 },
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152- "id" : " c8f51c8b "
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153153 "metadata" : {},
154154 "source" : [
155155 " Then, use the `Cleaner` to sanitize the data so that:\n "
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211211 " We can inspect which columns were dropped and what transformations were applied:"
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Original file line number Diff line number Diff line change 22 "cells" : [
33 {
44 "cell_type" : " markdown"
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66 "metadata" : {},
77 "source" : [
88 " # Exercise: using selectors together with `ApplyToCols`\n "
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2929 "outputs" : [],
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4646 },
4747 {
4848 "cell_type" : " markdown"
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5050 "metadata" : {},
5151 "source" : [
5252 " Using the skrub selectors and `ApplyToCols`:\n "
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6060 "cell_type" : " code"
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8484 {
8585 "cell_type" : " code"
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103103 },
104104 {
105105 "cell_type" : " markdown"
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107107 "metadata" : {},
108108 "source" : [
109109 " Given the same dataframe and using selectors, drop only string columns that contain\n "
113113 {
114114 "cell_type" : " code"
115115 "execution_count" : null ,
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118118 "outputs" : [],
119119 "source" : [
132132 {
133133 "cell_type" : " code"
134134 "execution_count" : null ,
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137137 "outputs" : [],
138138 "source" : [
143143 },
144144 {
145145 "cell_type" : " markdown"
146- "id" : " d3f434c8 "
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147147 "metadata" : {},
148148 "source" : [
149149 " Now write a custom function that selects columns where all values are lower than\n "
153153 {
154154 "cell_type" : " code"
155155 "execution_count" : null ,
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156+ "id" : " 6954ae31 "
157157 "metadata" : {},
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159159 "source" : [
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195195 "source" : []
Original file line number Diff line number Diff line change 22 "cells" : [
33 {
44 "cell_type" : " markdown"
5- "id" : " 564b4538 "
5+ "id" : " 49231767 "
66 "metadata" : {},
77 "source" : [
88 " # Exercise\n "
1919 {
2020 "cell_type" : " code"
2121 "execution_count" : null ,
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2323 "metadata" : {},
2424 "outputs" : [],
2525 "source" : [
2929 {
3030 "cell_type" : " code"
3131 "execution_count" : null ,
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3333 "metadata" : {},
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5353 {
5454 "cell_type" : " code"
5555 "execution_count" : null ,
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100100 {
101101 "cell_type" : " code"
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106106 "source" : [
120120 },
121121 {
122122 "cell_type" : " markdown"
123- "id" : " 145332dd "
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124124 "metadata" : {},
125125 "source" : [
126126 " Modify the script so that the `DatetimeEncoder` adds periodic encoding with sine\n "
130130 {
131131 "cell_type" : " code"
132132 "execution_count" : null ,
133- "id" : " 9340e84c "
133+ "id" : " f28f8625 "
134134 "metadata" : {},
135135 "outputs" : [],
136136 "source" : [
153153 },
154154 {
155155 "cell_type" : " markdown"
156- "id" : " 3ee9e75a "
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157157 "metadata" : {},
158158 "source" : [
159159 " Now modify the script above to add spline features (`periodic_encoding=\" spline\" `).\n "
162162 {
163163 "cell_type" : " code"
164164 "execution_count" : null ,
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167167 "outputs" : [],
168168 "source" : [
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192192 "metadata" : {},
193193 "outputs" : [],
194194 "source" : []
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