122 lines
2.6 KiB
Markdown
122 lines
2.6 KiB
Markdown
# [Pandas](https://pandas.pydata.org/)
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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```shell
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pip install pandas
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```
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## [Pandas](https://pandas.pydata.org/)
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The two most important data types of Pandas are:
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* Series
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* Data Frames
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> “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.”
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It is the basis for:
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* [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)
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> 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.
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* [Pingouin](https://pingouin-stats.org/build/html/index.html)
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> Pingouin is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy.
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* [rPy2](https://rpy2.github.io/)
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> rpy2 is an interface to R running embedded in a Python process.
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## [Pandas.Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas-series)
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```python
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class pandas.Series(data=None, index=None, dtype=None, name=None, copy=None, fastpath=False)
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```
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> One-dimensional ndarray with axis labels (including time series).
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>
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> 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).
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>
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> 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.
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Examples:
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```python
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import pandas as pd
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example = pd.Series(["Bambu", "Tree", "Sleep"])
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print(example)
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```
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Output:
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```python
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0 Bambu
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1 Tree
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2 Sleep
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dtype: object
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```
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```python
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import numpy as np
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import pandas as pd
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example = pd.Series([99, 88, 32])
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print(example)
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```
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Output:
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```python
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0 99
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1 88
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2 32
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dtype: int64
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```
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```python
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import numpy as np
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import pandas as pd
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rng = np.random.default_rng()
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a = rng.random((5))
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example = pd.Series(a)
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print(example)
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```
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Output:
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```python
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0 0.305920
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1 0.633360
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2 0.219094
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3 0.005722
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4 0.006673
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dtype: float64
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```
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```python
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import pandas as pd
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example = pd.Series(["Bambu", 3, "Sleep"])
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print(example)
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```
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Output:
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```python
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0 Bambu
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1 3
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2 Sleep
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dtype: object
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```
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