File size: 6,028 Bytes
745e7ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
define({
'name' :'pandas',
'sub-menu' : [
{
'name' : 'Setup',
'snippet' : [
'from __future__ import print_function, division',
'import pandas as pd',
],
},
{
'name' : 'Documentation',
'external-link' : 'http://pandas.pydata.org/pandas-docs/stable/',
},
'---',
{
'name' : 'Set options',
'snippet' : [
'pd.set_option(""display.height"", 10)',
'pd.set_option(""display.max_rows"", 20)',
'pd.set_option(""display.max_columns"", 500)',
'pd.set_option(""display.width"", 1000)',
],
},
{
'name' : 'To/from file',
'sub-menu' : [
{
'name' : 'Read from CSV',
'snippet' : [
'bp_data = pd.read_csv("path/to/file.csv", header=1, delim_whitespace=True)',
],
},
{
'name' : 'Write to CSV',
'snippet' : ['bp_data.to_csv("path/to/new_file.csv", sep=" ", header=False, index=False)',],
},
],
},
{
'name' : 'Deal with NaNs',
'sub-menu' : [
{
'name' : 'Filter out NaNs',
'snippet' : ['bp_data = bp_data.dropna()',],
},
{
'name' : 'Replace NaNs with number',
'snippet' : ['bp_data = bp_data.fillna(0.0)',],
},
],
},
{
'name' : 'Select rows',
'snippet' : ['bp_data[:5]',],
},
{
'name' : 'Select by column',
'snippet' : ['bp_column = bp_data[["Column name"]]',],
'sub-menu' : [
{
'name' : 'Select single column',
'snippet' : ['bp_column = bp_data[["Column name"]]',],
},
{
'name' : 'Select multiple columns',
'snippet' : [
'bp_columns = bp_data[["Column name 1", "Column name 2", "Column name 3"]]',],
},
],
},
{
'name' : 'Get numerical values from selection',
'sub-menu' : [
{
'name' : 'Select single column',
'snippet' : ['bp_num_value = bp_data[["Numerical column"]].values',],
},
{
'name' : 'Select multiple columns',
'snippet' : [
'bp_num_values = bp_data[["Numerical column 1", "Numerical column 2"]].values',],
},
{
'name' : 'Select rows',
'snippet' : ['bp_num_value = bp_data[:5].values',],
},
],
},
{
'name' : 'Iteration',
'snippet' : ['',],
},
{
'name' : 'Grouping',
'snippet' : ['',],
},
{
'name' : 'Sorting',
'snippet' : ['',],
},
{
'name' : 'Combining',
'sub-menu' : [
{
'name' : 'Split-apply-combine (sum)',
'snippet' : ['df['label_count'] = df.groupby('label', as_index=False)['label'].transform(lambda x: x.count())',],
},
{
'name' : 'Split-apply-combine (mean)',
'snippet' : ['df['label_mean'] = df.groupby('label', as_index=False)['label'].transform(lambda x: x.mean())',],
},
],
},
{
'name' : 'Basic stats',
'sub-menu' : [
{
'name' : 'Mean',
'snippet' : ['bp_mean = bp_data[["Numerical column 1"]].mean()',],
},
{
'name' : 'Mode',
'snippet' : ['bp_mode = bp_data[["Numerical column 1"]].mode()',],
},
{
'name' : 'Median',
'snippet' : ['bp_median = bp_data[["Numerical column 1"]].median()',],
},
{
'name' : 'Standard deviation (unbiased)',
'snippet' : ['bp_std = bp_data[["Numerical column 1"]].std()',],
},
{
'name' : 'Variance (unbiased)',
'snippet' : ['bp_var = bp_data[["Numerical column 1"]].var()',],
},
{
'name' : 'Skew (unbiased)',
'snippet' : ['bp_skew = bp_data[["Numerical column 1"]].skew()',],
},
{
'name' : 'Kurtosis (unbiased)',
'snippet' : ['bp_kurtosis = bp_data[["Numerical column 1"]].kurt()',],
},
{
'name' : 'Min',
'snippet' : ['bp_min = bp_data[["Numerical column 1"]].min()',],
},
{
'name' : 'Max',
'snippet' : ['bp_max = bp_data[["Numerical column 1"]].max()',],
},
{
'name' : 'Sum',
'snippet' : ['bp_sum = bp_data[["Numerical column 1"]].sum()',],
},
{
'name' : 'Product',
'snippet' : ['bp_product = bp_data[["Numerical column 1"]].product()',],
},
{
'name' : 'Number of elements',
'snippet' : ['bp_count = bp_data[["Numerical column 1"]].count()',],
},
],
},
],
});
|