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redframes
Pandas Version PyPI Downloads

About

redframes (rectangular data 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, compatible with scikit-learn, and works great with matplotlib.

Install & Import

pip install redframes
import redframes as rf

Quickstart

Copy-and-paste this to get started:

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:

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:

# 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 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 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:

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:

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"]);

redframes

scikit-learn

rf.DataFrame objects are fully compatible with sklearn functions, estimators, and transformers:

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.])