Display & options

console.log(df) prints the same text table as pandas. Display options control truncation and number formatting. They accept pandas' names (display.max_rows) or camelCase (maxRows).

import * as pd from 'pandasx';

const df = new pd.DataFrame({ x: Array.from({ length: 100 }, (_, i) => i / 7), y: Array.from({ length: 100 }, (_, i) => `row ${i}`) });

pd.setOption('display.max_rows', 6);
pd.setOption('display.precision', 3);
console.log(df);
console.log(pd.getOption('display.max_rows'));

pd.resetOption('all');
pd.optionContext({ 'display.precision': 1 }, () => console.log(df.head(2)));   // temporary
console.log(df.head(2));

          x       y
0     0.000   row 0
1     0.143   row 1
2     0.286   row 2
...     ...     ...
97   13.857  row 97
98   14.000  row 98
99   14.143  row 99

[100 rows x 2 columns]
6
     x      y
0  0.0  row 0
1  0.1  row 1
          x      y
0  0.000000  row 0
1  0.142857  row 1

Option	Default	Meaning
display.max_rows	60	Rows shown before truncating
display.min_rows	10	Rows shown when truncated
display.max_columns	20	Columns shown before truncating
display.precision	6	Decimal places for floats
display.width	80	Line width; wider frames hide middle columns
display.max_colwidth	50	Longest cell text before it is cut with ...
display.float_format	null	A function (v: number) => string
display.chop_threshold	null	Floats smaller than this (in absolute value) show as 0
display.show_dimensions	'truncate'	Show [n rows x m columns]: 'truncate', true or false
display.colheader_justify	'right'	Header alignment

pd.options is the live options object, so pd.options.precision = 2 also works.

Per-call rendering

toString takes the same settings for a single rendering:

import * as pd from 'pandasx';

const df = new pd.DataFrame({ a: [1.23456, null], b: ['x', 'y'] });
console.log(df.toString({ index: false, naRep: '-' }));
console.log(df.toString({ columns: ['b'], header: false }));

       a b
 1.23456 x
       - y
0  x
1  y

Inspecting a frame

import * as pd from 'pandasx';

const df = new pd.DataFrame({ id: [1, 2, 3], name: ['Ana', null, 'Carla'], score: [9.5, 7, null] });
console.log(df.info());
console.log(df.memoryUsage({ deep: true }));
console.log(df.shape, df.size);

<DataFrame>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #  Column  Non-Null Count  Dtype
 0  id      3 non-null      int64
 1  name    2 non-null      string
 2  score   2 non-null      float64
dtypes: int64(1), string(1), float64(1)
memory usage: 204.0+ bytes
Index  132
id      24
name   130
score   24
dtype: int64
[ 3, 3 ] 9