DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)
[source]
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage.
Parameters: |
verbose : bool, optional Whether to print the full summary. By default, the setting in buf : writable buffer, defaults to sys.stdout Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output. max_cols : int, optional When to switch from the verbose to the truncated output. If the DataFrame has more than memory_usage : bool, str, optional Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the True always show memory usage. False never shows memory usage. A value of ‘deep’ is equivalent to “True with deep introspection”. Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. null_counts : bool, optional Whether to show the non-null counts. By default, this is shown only if the frame is smaller than |
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Returns: |
None This method prints a summary of a DataFrame and returns None. |
See also
DataFrame.describe
DataFrame.memory_usage
>>> int_values = [1, 2, 3, 4, 5] >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon'] >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0] >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values, ... "float_col": float_values}) >>> df int_col text_col float_col 0 1 alpha 0.00 1 2 beta 0.25 2 3 gamma 0.50 3 4 delta 0.75 4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True) <class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Data columns (total 3 columns): int_col 5 non-null int64 text_col 5 non-null object float_col 5 non-null float64 dtypes: float64(1), int64(1), object(1) memory usage: 200.0+ bytes
Prints a summary of columns count and its dtypes but not per column information:
>>> df.info(verbose=False) <class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Columns: 3 entries, int_col to float_col dtypes: float64(1), int64(1), object(1) memory usage: 200.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:
>>> import io >>> buffer = io.StringIO() >>> df.info(buf=buffer) >>> s = buffer.getvalue() >>> with open("df_info.txt", "w", encoding="utf-8") as f: ... f.write(s) 260
The memory_usage
parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6) >>> df = pd.DataFrame({ ... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6), ... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6), ... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6) ... }) >>> df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 1000000 entries, 0 to 999999 Data columns (total 3 columns): column_1 1000000 non-null object column_2 1000000 non-null object column_3 1000000 non-null object dtypes: object(3) memory usage: 22.9+ MB
>>> df.info(memory_usage='deep') <class 'pandas.core.frame.DataFrame'> RangeIndex: 1000000 entries, 0 to 999999 Data columns (total 3 columns): column_1 1000000 non-null object column_2 1000000 non-null object column_3 1000000 non-null object dtypes: object(3) memory usage: 188.8 MB
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.info.html