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MarcoGorelliMarcoGorelli
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add examples
1 parent 0f6e2c3 commit 321db90

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+103
-82
lines changed

3 files changed

+103
-82
lines changed

‎pandas/core/frame.py

+83
Original file line numberDiff line numberDiff line change
@@ -2232,6 +2232,89 @@ def to_html(
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is used. By default, the setting in
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``pandas.options.display.max_info_columns`` is used.
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""",
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examples_sub="""
2236+
>>> int_values = [1, 2, 3, 4, 5]
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>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
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>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
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>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
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... "float_col": float_values})
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>>> df
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int_col text_col float_col
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0 1 alpha 0.00
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1 2 beta 0.25
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2 3 gamma 0.50
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3 4 delta 0.75
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4 5 epsilon 1.00
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Prints information of all columns:
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2251+
>>> df.info(verbose=True)
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<class 'pandas.core.frame.DataFrame'>
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RangeIndex: 5 entries, 0 to 4
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Data columns (total 3 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 int_col 5 non-null int64
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1 text_col 5 non-null object
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2 float_col 5 non-null float64
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dtypes: float64(1), int64(1), object(1)
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memory usage: 248.0+ bytes
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Prints a summary of columns count and its dtypes but not per column
2264+
information:
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>>> df.info(verbose=False)
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<class 'pandas.core.frame.DataFrame'>
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RangeIndex: 5 entries, 0 to 4
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Columns: 3 entries, int_col to float_col
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dtypes: float64(1), int64(1), object(1)
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memory usage: 248.0+ bytes
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Pipe output of DataFrame.info to buffer instead of sys.stdout, get
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buffer content and writes to a text file:
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>>> import io
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>>> buffer = io.StringIO()
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>>> df.info(buf=buffer)
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>>> s = buffer.getvalue()
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>>> with open("df_info.txt", "w",
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... encoding="utf-8") as f: # doctest: +SKIP
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... f.write(s)
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260
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The `memory_usage` parameter allows deep introspection mode, specially
2286+
useful for big DataFrames and fine-tune memory optimization:
2287+
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>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
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>>> df = pd.DataFrame({
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... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
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... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
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... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
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... })
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>>> df.info()
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<class 'pandas.core.frame.DataFrame'>
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RangeIndex: 1000000 entries, 0 to 999999
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Data columns (total 3 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 column_1 1000000 non-null object
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1 column_2 1000000 non-null object
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2 column_3 1000000 non-null object
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dtypes: object(3)
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memory usage: 22.9+ MB
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>>> df.info(memory_usage='deep')
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<class 'pandas.core.frame.DataFrame'>
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RangeIndex: 1000000 entries, 0 to 999999
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Data columns (total 3 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 column_1 1000000 non-null object
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1 column_2 1000000 non-null object
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2 column_3 1000000 non-null object
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dtypes: object(3)
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memory usage: 188.8 MB
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""",
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)
22362319
@Appender(NDFrame.info.__doc__)
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def info(

‎pandas/core/generic.py

+1-81
Original file line numberDiff line numberDiff line change
@@ -1792,87 +1792,7 @@ def info(
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17931793
Examples
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--------
1795-
>>> int_values = [1, 2, 3, 4, 5]
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>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
1797-
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
1798-
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
1799-
... "float_col": float_values})
1800-
>>> df
1801-
int_col text_col float_col
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0 1 alpha 0.00
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1 2 beta 0.25
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2 3 gamma 0.50
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3 4 delta 0.75
1806-
4 5 epsilon 1.00
1807-
1808-
Prints information of all columns:
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1810-
>>> df.info(verbose=True)
1811-
<class 'pandas.core.frame.DataFrame'>
1812-
RangeIndex: 5 entries, 0 to 4
1813-
Data columns (total 3 columns):
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# Column Non-Null Count Dtype
1815-
--- ------ -------------- -----
1816-
0 int_col 5 non-null int64
1817-
1 text_col 5 non-null object
1818-
2 float_col 5 non-null float64
1819-
dtypes: float64(1), int64(1), object(1)
1820-
memory usage: 248.0+ bytes
1821-
1822-
Prints a summary of columns count and its dtypes but not per column
1823-
information:
1824-
1825-
>>> df.info(verbose=False)
1826-
<class 'pandas.core.frame.DataFrame'>
1827-
RangeIndex: 5 entries, 0 to 4
1828-
Columns: 3 entries, int_col to float_col
1829-
dtypes: float64(1), int64(1), object(1)
1830-
memory usage: 248.0+ bytes
1831-
1832-
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
1833-
buffer content and writes to a text file:
1834-
1835-
>>> import io
1836-
>>> buffer = io.StringIO()
1837-
>>> df.info(buf=buffer)
1838-
>>> s = buffer.getvalue()
1839-
>>> with open("df_info.txt", "w",
1840-
... encoding="utf-8") as f: # doctest: +SKIP
1841-
... f.write(s)
1842-
260
1843-
1844-
The `memory_usage` parameter allows deep introspection mode, specially
1845-
useful for big DataFrames and fine-tune memory optimization:
1846-
1847-
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
1848-
>>> df = pd.DataFrame({
1849-
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
1850-
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
1851-
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
1852-
... })
1853-
>>> df.info()
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<class 'pandas.core.frame.DataFrame'>
1855-
RangeIndex: 1000000 entries, 0 to 999999
1856-
Data columns (total 3 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 column_1 1000000 non-null object
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1 column_2 1000000 non-null object
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2 column_3 1000000 non-null object
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dtypes: object(3)
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memory usage: 22.9+ MB
1864-
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>>> df.info(memory_usage='deep')
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<class 'pandas.core.frame.DataFrame'>
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RangeIndex: 1000000 entries, 0 to 999999
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Data columns (total 3 columns):
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# Column Non-Null Count Dtype
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--- ------ -------------- -----
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0 column_1 1000000 non-null object
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1 column_2 1000000 non-null object
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2 column_3 1000000 non-null object
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dtypes: object(3)
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memory usage: 188.8 MB
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%(examples_sub)s
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"""
18771797
if buf is None: # pragma: no cover
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buf = sys.stdout

‎pandas/core/series.py

+19-1
Original file line numberDiff line numberDiff line change
@@ -4182,7 +4182,25 @@ def replace(
41824182
method=method,
41834183
)
41844184

4185-
@Substitution(klass="Series", type_sub="", max_cols_sub="")
4185+
@Substitution(
4186+
klass="Series",
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type_sub="",
4188+
max_cols_sub="",
4189+
examples_sub="""
4190+
>>> int_values = [1, 2, 3, 4, 5]
4191+
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
4192+
>>> s = pd.Series(text_values, index=int_values)
4193+
>>> s.info()
4194+
<class 'pandas.core.series.Series'>
4195+
Int64Index: 5 entries, 1 to 5
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Series name: None
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# Non-Null Count Dtype
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--- -------------- -----
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0 5 non-null object
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dtypes: object(1)
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memory usage: 80.0+ bytes
4202+
""",
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)
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@Appender(NDFrame.info.__doc__)
41874205
def info(
41884206
self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None

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