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https://github.com/maxhumber/redframes
General Purpose Data Manipulation Library
https://github.com/maxhumber/redframes
data-science pandas python
Last synced: about 2 months ago
JSON representation
General Purpose Data Manipulation Library
- Host: GitHub
- URL: https://github.com/maxhumber/redframes
- Owner: maxhumber
- License: bsd-2-clause
- Created: 2022-08-21T14:28:24.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-03-17T22:29:02.000Z (over 1 year ago)
- Last Synced: 2024-07-17T23:55:43.840Z (2 months ago)
- Topics: data-science, pandas, python
- Language: Python
- Homepage:
- Size: 1.84 MB
- Stars: 298
- Watchers: 6
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-dataframes - redframes - [re]ctangular[d]ata[frames] (Libraries)
README
### About
**redframes** (**re**ctangular **d**ata **frames**) is a general purpose data manipulation library that prioritizes syntax, simplicity, and speed (to a solution). Importantly, the library is fully interoperable with [pandas](https://github.com/pandas-dev/pandas), compatible with [scikit-learn](https://github.com/scikit-learn/scikit-learn), and works great with [matplotlib](https://github.com/matplotlib/matplotlib).
### Install & Import
```sh
pip install redframes
```
```python
import redframes as rf
```
### Quickstart
Copy-and-paste this to get started:
```python
import redframes as rf
df = rf.DataFrame({
'bear': ['Brown bear', 'Polar bear', 'Asian black bear', 'American black bear', 'Sun bear', 'Sloth bear', 'Spectacled bear', 'Giant panda'],
'genus': ['Ursus', 'Ursus', 'Ursus', 'Ursus', 'Helarctos', 'Melursus', 'Tremarctos', 'Ailuropoda'],
'weight (male, lbs)': ['300-860', '880-1320', '220-440', '125-500', '60-150', '175-310', '220-340', '190-275'],
'weight (female, lbs)': ['205-455', '330-550', '110-275', '90-300', '45-90', '120-210', '140-180', '155-220']
})
# | bear | genus | weight (male, lbs) | weight (female, lbs) |
# |:--------------------|:-----------|:---------------------|:-----------------------|
# | Brown bear | Ursus | 300-860 | 205-455 |
# | Polar bear | Ursus | 880-1320 | 330-550 |
# | Asian black bear | Ursus | 220-440 | 110-275 |
# | American black bear | Ursus | 125-500 | 90-300 |
# | Sun bear | Helarctos | 60-150 | 45-90 |
# | Sloth bear | Melursus | 175-310 | 120-210 |
# | Spectacled bear | Tremarctos | 220-340 | 140-180 |
# | Giant panda | Ailuropoda | 190-275 | 155-220 |
(
df
.rename({"weight (male, lbs)": "male", "weight (female, lbs)": "female"})
.gather(["male", "female"], into=("sex", "weight"))
.split("weight", into=["min", "max"], sep="-")
.gather(["min", "max"], into=("stat", "weight"))
.mutate({"weight": lambda row: float(row["weight"])})
.group(["genus", "sex"])
.rollup({"weight": ("weight", rf.stat.mean)})
.spread("sex", using="weight")
.mutate({"dimorphism": lambda row: round(row["male"] / row["female"], 2)})
.drop(["male", "female"])
.sort("dimorphism", descending=True)
)
# | genus | dimorphism |
# |:-----------|-------------:|
# | Ursus | 2.01 |
# | Tremarctos | 1.75 |
# | Helarctos | 1.56 |
# | Melursus | 1.47 |
# | Ailuropoda | 1.24 |
```
For comparison, here's the equivalent pandas:
```python
import pandas as pd
# df = pd.DataFrame({...})
df = df.rename(columns={"weight (male, lbs)": "male", "weight (female, lbs)": "female"})
df = pd.melt(df, id_vars=['bear', 'genus'], value_vars=['male', 'female'], var_name='sex', value_name='weight')
df[["min", "max"]] = df["weight"].str.split("-", expand=True)
df = df.drop("weight", axis=1)
df = pd.melt(df, id_vars=['bear', 'genus', 'sex'], value_vars=['min', 'max'], var_name='stat', value_name='weight')
df['weight'] = df["weight"].astype('float')
df = df.groupby(["genus", "sex"])["weight"].mean()
df = df.reset_index()
df = pd.pivot_table(df, index=['genus'], columns=['sex'], values='weight')
df = df.reset_index()
df = df.rename_axis(None, axis=1)
df["dimorphism"] = round(df["male"] / df["female"], 2)
df = df.drop(["female", "male"], axis=1)
df = df.sort_values("dimorphism", ascending=False)
df = df.reset_index(drop=True)
# 🤮
```
### IO
Save, load, and convert `rf.DataFrame` objects:
```python
# save .csv
rf.save(df, "bears.csv")
# load .csv
df = rf.load("bears.csv")
# convert redframes → pandas
pandas_df = rf.unwrap(df)
# convert pandas → redframes
df = rf.wrap(pandas_df)
```
### Verbs
Verbs are [pure](https://en.wikipedia.org/wiki/Pure_function) and "chain-able" methods that manipulate `rf.DataFrame` objects. Here is the complete list (see *docstrings* for examples and more details):
| Verb | Description |
| ------------------------------------------------ | ------------------------------------------------------------ |
| `accumulate`‡ | Run a cumulative sum over a column |
| `append` | Append rows from another DataFrame |
| `combine` | Combine multiple columns into a single column (opposite of `split`) |
| `cross` | Cross join columns from another DataFrame |
| `dedupe` | Remove duplicate rows |
| [`denix`](https://www.dictionary.com/browse/nix) | Remove rows with missing values |
| `drop` | Drop entire columns (opposite of `select`) |
| `fill` | Fill missing values "down", "up", or with a constant |
| `filter` | Keep rows matching specific conditions |
| `gather`‡ | Gather columns into rows (opposite of `spread`) |
| `group` | Prepare groups for compatible verbs‡ |
| `join` | Join columns from another DataFrame |
| `mutate` | Create a new, or overwrite an existing column |
| `pack`‡ | Collate and concatenate row values for a target column (opposite of `unpack`) |
| `rank`‡ | Rank order values in a column |
| `rename` | Rename column keys |
| `replace` | Replace matching values within columns |
| `rollup`‡ | Apply summary functions and/or statistics to target columns |
| `sample` | Randomly sample any number of rows |
| `select` | Select specific columns (opposite of `drop`) |
| `shuffle` | Shuffle the order of all rows |
| `sort` | Sort rows by specific columns |
| `split` | Split a single column into multiple columns (opposite of `combine`) |
| `spread` | Spread rows into columns (opposite of `gather`) |
| `take`‡ | Take any number of rows (from the top/bottom) |
| `unpack` | "Explode" concatenated row values into multiple rows (opposite of `pack`) |
### Properties
In addition to all of the verbs there are several properties attached to each `DataFrame` object:
```python
df["genus"]
# ['Ursus', 'Ursus', 'Ursus', 'Ursus', 'Helarctos', 'Melursus', 'Tremarctos', 'Ailuropoda']
df.columns
# ['bear', 'genus', 'weight (male, lbs)', 'weight (female, lbs)']
df.dimensions
# {'rows': 8, 'columns': 4}
df.empty
# False
df.memory
# '2 KB'
df.types
# {'bear': object, 'genus': object, 'weight (male, lbs)': object, 'weight (female, lbs)': object}
```
### matplotlib
`rf.DataFrame` objects integrate seamlessly with `matplotlib`:
```python
import redframes as rf
import matplotlib.pyplot as plt
football = rf.DataFrame({
'position': ['TE', 'K', 'RB', 'WR', 'QB'],
'avp': [116.98, 131.15, 180, 222.22, 272.91]
})
df = (
football
.mutate({"color": lambda row: row["position"] in ["WR", "RB"]})
.replace({"color": {False: "orange", True: "red"}})
)
plt.barh(df["position"], df["avp"], color=df["color"]);
```
### scikit-learn
`rf.DataFrame` objects are fully compatible with `sklearn` functions, estimators, and transformers:
```python
import redframes as rf
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
df = rf.DataFrame({
"touchdowns": [15, 19, 5, 7, 9, 10, 12, 22, 16, 10],
"age": [21, 22, 21, 24, 26, 28, 30, 35, 28, 21],
"mvp": [1, 1, 0, 0, 0, 0, 0, 1, 0, 0]
})
target = "touchdowns"
y = df[target]
X = df.drop(target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
model = LinearRegression()
model.fit(X_train, y_train)
model.score(X_test, y_test)
# 0.5083194901655527
print(X_train.take(1))
# rf.DataFrame({'age': [21], 'mvp': [0]})
X_new = rf.DataFrame({'age': [22], 'mvp': [1]})
model.predict(X_new)
# array([19.])
```