@@ -32,6 +32,7 @@ define([
3232 "name" : "Series" ,
3333 "library" : "pandas" ,
3434 "description" : "1 dimension array with same data types" ,
35+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.Series.html" ,
3536 "code" : "${o0} = pd.Series(${i0}${index}${name})" ,
3637 "options" : [
3738 {
@@ -64,6 +65,7 @@ define([
6465 "name" : "DataFrame" ,
6566 "library" : "pandas" ,
6667 "description" : "2 dimension data table type pandas variable" ,
68+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html" ,
6769 "code" : "${o0} = pd.DataFrame(${i0}${index}${columns})" ,
6870 "options" : [
6971 {
@@ -98,6 +100,7 @@ define([
98100 "name" : "Index" ,
99101 "library" : "pandas" ,
100102 "description" : "Create index object" ,
103+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.Index.html" ,
101104 "code" : "${o0} = pd.Index(${data}${dtype}${copy}${name}${tupleize_cols})" ,
102105 "options" : [
103106 {
@@ -162,6 +165,7 @@ define([
162165 "name" : "Read CSV" ,
163166 "library" : "pandas" ,
164167 "description" : "" ,
168+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html" ,
165169 "code" : "${o0} = pd.read_csv(${i0}${encoding}${header}${sep}${names}${usecols}${index_col}${na_values}${skiprows}${nrows}${chunksize}${etc})" ,
166170 "options" : [
167171 {
@@ -250,6 +254,7 @@ define([
250254 "name" : "To CSV" ,
251255 "library" : "pandas" ,
252256 "description" : "dataframe to csv" ,
257+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html" ,
253258 "code" : "${i0}.to_csv(${i1}${encoding}${header}${index}${sep}${na_rep}${columns}${etc})" ,
254259 "options" : [
255260 {
@@ -320,6 +325,7 @@ define([
320325 "name" : "Merge" ,
321326 "library" : "pandas" ,
322327 "description" : "Merge 2 objects" ,
328+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.merge.html" ,
323329 "code" : "${o0} = pd.merge(${i0}, ${i1}${left_on}${right_on}${how}${sort})" ,
324330 "options" : [
325331 {
@@ -390,6 +396,7 @@ define([
390396 "name" : "Join" ,
391397 "library" : "pandas" ,
392398 "description" : "Merge multiple objects" ,
399+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html" ,
393400 "code" : "${o0} = ${i0}.join(${i1}${on}${how}${sort}${lsuffix}${rsuffix})" ,
394401 "options" : [
395402 {
@@ -469,6 +476,7 @@ define([
469476 "name" : "Concat" ,
470477 "library" : "pandas" ,
471478 "description" : "Merge multiple objects" ,
479+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.concat.html" ,
472480 "code" : "${o0} = pd.concat([${i0}]${index}${axis}${sort}${join})" ,
473481 "options" : [
474482 {
@@ -540,6 +548,7 @@ define([
540548 "name" : "Sort By Index" ,
541549 "library" : "pandas" ,
542550 "description" : "Sort by index" ,
551+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html" ,
543552 "code" : "${o0} = ${i0}.sort_index(${axis}${ascending}${inplace}${kind})" ,
544553 "options" : [
545554 {
@@ -624,6 +633,7 @@ define([
624633 "name" : "Group By" ,
625634 "library" : "pandas" ,
626635 "description" : "Group DataFrame/Series" ,
636+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html" ,
627637 "code" : "${o0} = ${i0}.groupby(${level}${axis}${sort}${as_index})" ,
628638 "options" : [
629639 {
@@ -692,6 +702,7 @@ define([
692702 "name" : "Period" ,
693703 "library" : "pandas" ,
694704 "description" : "Create Period object" ,
705+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.Period.html" ,
695706 "code" : "${o0} = pd.Period(${i0}${freq}${year}${month}${day})" ,
696707 "options" : [
697708 {
@@ -759,6 +770,7 @@ define([
759770 "name" : "Drop NA" ,
760771 "library" : "pandas" ,
761772 "description" : "" ,
773+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html" ,
762774 "code" : "${o0} = ${i0}.dropna(${axis}${how}${thresh})" ,
763775 "options" : [
764776 {
@@ -823,6 +835,7 @@ define([
823835 "name" : "Fill NA" ,
824836 "library" : "pandas" ,
825837 "description" : "replace null using value" ,
838+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html" ,
826839 "code" : "${o0} = ${i0}.fillna(${value}${axis}${method}${inplace}${limit})" ,
827840 "options" : [
828841 {
@@ -902,6 +915,7 @@ define([
902915 "name" : "Get Duplicates" ,
903916 "library" : "pandas" ,
904917 "description" : "Get duplicates" ,
918+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.duplicated.html" ,
905919 "code" : "${o0} = ${i0}.duplicated(${keep})" ,
906920 "options" : [
907921 {
@@ -986,6 +1000,7 @@ define([
9861000 "name" : "Scala Replace" ,
9871001 "library" : "pandas" ,
9881002 "description" : "Replace scala value" ,
1003+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html" ,
9891004 "code" : "${o0} = ${i0}.replace(${to_replace}${value}${method})" ,
9901005 "options" : [
9911006 {
@@ -1038,6 +1053,7 @@ define([
10381053 "name" : "List-like Replace" ,
10391054 "library" : "pandas" ,
10401055 "description" : "Replace values using list" ,
1056+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html" ,
10411057 "code" : "${o0} = ${i0}.replace(${to_replace}${value}${method})" ,
10421058 "options" : [
10431059 {
@@ -1090,6 +1106,7 @@ define([
10901106 "name" : "Dict-like Replace" ,
10911107 "library" : "pandas" ,
10921108 "description" : "Replace values using dictionary" ,
1109+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html" ,
10931110 "code" : "${o0} = ${i0}.replace(${to_replace}${value}${method})" ,
10941111 "options" : [
10951112 {
@@ -1142,6 +1159,7 @@ define([
11421159 "name" : "Regular Expression Replace" ,
11431160 "library" : "pandas" ,
11441161 "description" : "" ,
1162+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html" ,
11451163 "code" : "${o0} = ${i0}.replace(${to_replace}${value}${method}${regex})" ,
11461164 "options" : [
11471165 {
@@ -1203,6 +1221,7 @@ define([
12031221 "name" : "Sum" ,
12041222 "library" : "pandas" ,
12051223 "description" : "" ,
1224+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html" ,
12061225 "code" : "${o0} = ${i0}.sum(${axis}${skipna}${level})" ,
12071226 "options" : [
12081227 {
@@ -1262,6 +1281,7 @@ define([
12621281 "name" : "Mean" ,
12631282 "library" : "pandas" ,
12641283 "description" : "" ,
1284+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mean.html" ,
12651285 "code" : "${o0} = ${i0}.mean(${axis}${skipna}${level})" ,
12661286 "options" : [
12671287 {
@@ -1321,6 +1341,7 @@ define([
13211341 "name" : "Count" ,
13221342 "library" : "pandas" ,
13231343 "description" : "Count except NA values" ,
1344+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.count.html" ,
13241345 "code" : "${o0} = ${i0}.count(${axis}${skipna}${level})" ,
13251346 "options" : [
13261347 {
@@ -1380,6 +1401,7 @@ define([
13801401 "name" : "Max" ,
13811402 "library" : "pandas" ,
13821403 "description" : "" ,
1404+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.max.html" ,
13831405 "code" : "${o0} = ${i0}.max(${axis}${skipna}${level})" ,
13841406 "options" : [
13851407 {
@@ -1439,6 +1461,7 @@ define([
14391461 "name" : "Min" ,
14401462 "library" : "pandas" ,
14411463 "description" : "" ,
1464+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.min.html" ,
14421465 "code" : "${o0} = ${i0}.min(${axis}${skipna}${level})" ,
14431466 "options" : [
14441467 {
@@ -1498,6 +1521,7 @@ define([
14981521 "name" : "Median" ,
14991522 "library" : "pandas" ,
15001523 "description" : "Median(50%)" ,
1524+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.median.html" ,
15011525 "code" : "${o0} = ${i0}.median(${axis}${skipna}${level}${numeric_only})" ,
15021526 "options" : [
15031527 {
@@ -1574,6 +1598,7 @@ define([
15741598 "name" : "Std" ,
15751599 "library" : "pandas" ,
15761600 "description" : "" ,
1601+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.std.html" ,
15771602 "code" : "${o0} = ${i0}.std(${axis}${skipna}${level}${numeric_only})" ,
15781603 "options" : [
15791604 {
@@ -1650,6 +1675,7 @@ define([
16501675 "name" : "Quantile" ,
16511676 "library" : "pandas" ,
16521677 "description" : "Calculate quantile between 0 and 1" ,
1678+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html" ,
16531679 "code" : "${o0} = ${i0}.quantile(${q}${axis}${numeric_only}${interpolation})" ,
16541680 "options" : [
16551681 {
@@ -1735,6 +1761,7 @@ define([
17351761 "name" : "Drop Row/Column" ,
17361762 "library" : "pandas" ,
17371763 "description" : "Drop row and column" ,
1764+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html" ,
17381765 "code" : "${o0} = ${i0}.drop(${i1}${axis})" ,
17391766 "options" : [
17401767 {
@@ -1791,6 +1818,7 @@ define([
17911818 "name" : "date_range" ,
17921819 "library" : "pandas" ,
17931820 "description" : "Create DatetimeIndex type timestamp" ,
1821+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.date_range.html" ,
17941822 "code" : "${o0} = pd.date_range(${start}${end}${periods}${freq})" ,
17951823 "options" : [
17961824 {
@@ -1862,6 +1890,7 @@ define([
18621890 "name" : "Sort By Values" ,
18631891 "library" : "pandas" ,
18641892 "description" : "" ,
1893+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html" ,
18651894 "code" : "${o0} = ${i0}.sort_values(${by}${axis}${ascending}${inplace}${kind})" ,
18661895 "options" : [
18671896 {
@@ -1946,6 +1975,7 @@ define([
19461975 "name" : "Is Null" ,
19471976 "library" : "pandas" ,
19481977 "description" : "Find null" ,
1978+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.isnull.html" ,
19491979 "code" : "${o0} = pd.isnull(${i0})" ,
19501980 "options" : [
19511981 {
@@ -1974,6 +2004,7 @@ define([
19742004 "name" : "Not Null" ,
19752005 "library" : "pandas" ,
19762006 "description" : "Find not null" ,
2007+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.notnull.html" ,
19772008 "code" : "${o0} = pd.notnull(${i0})" ,
19782009 "options" : [
19792010 {
@@ -2002,6 +2033,7 @@ define([
20022033 "name" : "Transpose" ,
20032034 "library" : "pandas" ,
20042035 "description" : "Transpose row and column" ,
2036+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.T.html" ,
20052037 "code" : "${o0} = ${i0}.T" ,
20062038 "options" : [
20072039 {
@@ -2031,6 +2063,7 @@ define([
20312063 "name" : "Get columns" ,
20322064 "library" : "pandas" ,
20332065 "description" : "" ,
2066+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html" ,
20342067 "code" : "${o0} = ${i0}.columns" ,
20352068 "options" : [
20362069 {
@@ -2375,6 +2408,7 @@ define([
23752408 "name" : "Unique" ,
23762409 "library" : "pandas" ,
23772410 "description" : "" ,
2411+ "docs" : "https://pandas.pydata.org/docs/reference/api/pandas.Series.unique.html" ,
23782412 "code" : "${o0} = ${i0}.unique()" ,
23792413 "options" : [
23802414 {
0 commit comments