|
|
||
|---|---|---|
| .. | ||
| README.md | ||
Pandas
{:.no_toc}
* TOC {:toc}The goal
Questions to David Rotermund
pip install pandas
Pandas
The two most important data types of Pandas are:
- Series
- Data Frames
“Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.”
It is the basis for:
This module contains a large number of probability distributions, summary and frequency statistics, correlation functions and statistical tests, masked statistics, kernel density estimation, quasi-Monte Carlo functionality, and more.
Pingouin is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy.
rpy2 is an interface to R running embedded in a Python process.
Pandas.Series
class pandas.Series(data=None, index=None, dtype=None, name=None, copy=None, fastpath=False)
One-dimensional ndarray with axis labels (including time series).
Labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN).
Operations between Series (+, -, /, *, **) align values based on their associated index values– they need not be the same length. The result index will be the sorted union of the two indexes.
Example 1:
import pandas as pd
example = pd.Series(["Bambu", "Tree", "Sleep"])
print(example)
Output:
0 Bambu
1 Tree
2 Sleep
dtype: object
Example 2:
import numpy as np
import pandas as pd
example = pd.Series([99, 88, 32])
print(example)
Output:
0 99
1 88
2 32
dtype: int64
Example 3:
import numpy as np
import pandas as pd
rng = np.random.default_rng()
a = rng.random((5))
example = pd.Series(a)
print(example)
Output:
0 0.305920
1 0.633360
2 0.219094
3 0.005722
4 0.006673
dtype: float64
Example 4:
import pandas as pd
example = pd.Series(["Bambu", 3, "Sleep"])
print(example)
Output:
0 Bambu
1 3
2 Sleep
dtype: object
index and values
import pandas as pd
example = pd.Series(["Bambu", "Tree", "Sleep"])
print(example.index)
print()
print(example.values)
Output:
RangeIndex(start=0, stop=3, step=1)
['Bambu' 'Tree' 'Sleep']
DataFrame
class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.
pandas.concat
pandas.concat(objs, *, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=None)
Concatenate pandas objects along a particular axis.
Allows optional set logic along the other axes.
Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.
Saving (pandas.DataFrame.to_pickle) / loading (pandas.read_pickle) data ‘natively’
Save:
import pandas as pd
original_df = pd.DataFrame(
{"foo": range(5), "bar": range(5, 10)}
)
print(original_df)
pd.to_pickle(original_df, "./dummy.pkl")
Output:
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
Load:
import pandas as pd
unpickled_df = pd.read_pickle("./dummy.pkl")
print(unpickled_df)
Output:
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
read
pandas.read_pickle(filepath_or_buffer, compression='infer', storage_options=None)
Load pickled pandas object (or any object) from file.
write
DataFrame.to_pickle(path, compression='infer', protocol=5, storage_options=None)
Pickle (serialize) object to file.
I/O operations
| Pickling |
| Flat file |
| Clipboard |
| Excel |
| JSON |
| HTML |
| XML |
| Latex |
| HDFStore: PyTables (HDF5) |
| Feather |
| Parquet |
| ORC |
| SAS |
| SPSS |
| SQL |
| Google BigQuery |
| STATA |
csv (“comma” separated values file)
read
pandas.read_csv(filepath_or_buffer, *, sep=_NoDefault.no_default, delimiter=None, header='infer', names=_NoDefault.no_default, index_col=None, usecols=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=None, infer_datetime_format=_NoDefault.no_default, keep_date_col=False, date_parser=_NoDefault.no_default, date_format=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, on_bad_lines='error', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None, dtype_backend=_NoDefault.no_default)
Read a comma-separated values (csv) file into DataFrame.
write
DataFrame.to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', lineterminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)
Write object to a comma-separated values (csv) file.
Excel
read
pandas.read_excel(io, sheet_name=0, *, header=0, names=None, index_col=None, usecols=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=_NoDefault.no_default, date_format=None, thousands=None, decimal='.', comment=None, skipfooter=0, storage_options=None, dtype_backend=_NoDefault.no_default, engine_kwargs=None)
Read an Excel file into a pandas DataFrame.
Supports xls, xlsx, xlsm, xlsb, odf, ods and odt file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets.
write
DataFrame.to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, inf_rep='inf', freeze_panes=None, storage_options=None, engine_kwargs=None)
Write object to an Excel sheet.
To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.
Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.
JSON
read
pandas.read_json(path_or_buf, *, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates=True, precise_float=False, date_unit=None, encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer', nrows=None, storage_options=None, dtype_backend=_NoDefault.no_default, engine='ujson')
Convert a JSON string to pandas object.
write
DataFrame.to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=None, indent=None, storage_options=None, mode='w')
Convert the object to a JSON string.
Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.