Python DfLoaderの例

コード例 #1

ファイル: clause_has.py プロジェクト: CambridgeSemiticsLab/Gesenius_data

import sys
# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
both_df = DfLoad.eng_both()

run_analyses([
    {
        'name': 'has_objc',
        'df': both_df,
        'index': 'eng_TAMsimp',
        'columns': 'has_objc',
        'examples': [],
    },
    {
        'name': 'has_loca',
        'df': both_df,
        'index': 'eng_TAMsimp',
        'columns': ['has_loca'],
        'examples': [],
    },
    {
        'name': 'has_time',
        'df': both_df,
        'index': 'eng_TAMsimp',
        'columns': ['has_time'],
        'examples': [],

コード例 #2

ファイル: clause.py プロジェクト: CambridgeSemiticsLab/Gesenius_data

import sys
# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
eng_df = DfLoad.eng_agree()

# features needed for selections
main_genre = ['prose', 'poetry', 'prophetic']
main_dom = ['Q', 'N']

run_analyses([
    {
        'name': 'clause_type',
        'df': eng_df,
        'index': 'eng_TAM',
        'columns': 'clause_type',
    },
    {
        'name': 'clause_rela',
        'df': eng_df,
        'index': 'eng_TAM',
        'columns': 'clause_rela',
    },
    {
        'name': 'clause_rela',
        'df': eng_df,
        'index': 'eng_TAM',

コード例 #3

import sys
import pandas as pd

# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
agg_df = DfLoad.eng_simp_agree()
esv_df = DfLoad.esv()
niv_df = DfLoad.niv()
eng_df = DfLoad.eng_both()
disag_df_simp = DfLoad.eng_simp_disagree()
disag_df = DfLoad.eng_disagree()


def sum_top_values(df):
    """Sums the top values of dataframes."""
    top = 0.015
    pr_df = df / df.sum()
    top_pr = pr_df.loc[pr_df['sum'] >= top]
    top_ct = df.loc[top_pr.index]
    top_sum = pd.DataFrame(top_ct.sum())
    sum_pr = pd.DataFrame(top_pr.sum())
    data = {
        'top_ct': top_ct,
        'top_pr': top_pr,
        'top_sum': top_sum,
        'top_sum_pr': sum_pr,

コード例 #4

ファイル: translations.py プロジェクト: CambridgeSemiticsLab/Gesenius_data

import sys
# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
eng_df = DfLoad.eng_agree()
esv_df = DfLoad.esv()
niv_df = DfLoad.niv()
both_df = DfLoad.eng_both()
disag_df = DfLoad.eng_disagree()

run_analyses([
    {
        'name': 'eng_tenses',
        'df': eng_df,
        'index': 'eng_TAM',
    },
    {
        'name': 'esv_tenses',
        'df': esv_df,
        'index': 'esv_TAM',
    },
    {
        'name': 'niv_tenses',
        'df': niv_df,
        'index': 'niv_TAM',
    },
    {

コード例 #5

import pandas as pd
from load_dfs import DfLoader

DfLoad = DfLoader(
    '/Users/cody/github/CambridgeSemiticsLab/Gesenius_data/results/csv/qtl')

qatal_df = DfLoad.df_safe()
eng_df = DfLoad.eng_agree()
esv_df = DfLoad.esv()
niv_df = DfLoad.niv()
disag_df = DfLoad.eng_disagree()

print(disag_df.shape)
print(disag_df.head())

#test = pd.pivot_table(
#    eng_df,
#    index='eng_TAM',
#    columns='has_objc',
#    aggfunc='size',
#    fill_value=0,
#    #dropna=False,
#)

print(test)

コード例 #6

import sys
# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
esv_df = DfLoad.esv()

# features needed for selections
main_genre = ['prose', 'poetry', 'prophetic']
main_dom = ['Q', 'N']

run_analyses(
    [
        {
            'name': 'clause_type',
            'df': esv_df,
            'index': 'esv_TAM',
            'columns': 'clause_type',
        },
        {
            'name': 'clause_rela',
            'df': esv_df,
            'index': 'esv_TAM',
            'columns': 'clause_rela',
        },
        {
            'name': 'cltype_simp',
            'df': esv_df,

コード例 #7

import sys
# NB snakemake runs script from /workflow directory
sys.path.append('scripts/analysis')
from load_dfs import DfLoader
from analysis import run_analyses

# load the dataframes
DfLoad = DfLoader(snakemake.input.data_dir)
niv_df = DfLoad.niv()


# features needed for selections
main_genre = ['prose', 'poetry', 'prophetic']
main_dom = ['Q', 'N']

run_analyses([
   {
        'name': 'clause_type',
        'df': niv_df,
        'index': 'niv_TAM',
        'columns': 'clause_type',
    },
    {
        'name': 'clause_rela',
        'df': niv_df,
        'index': 'niv_TAM',
        'columns': 'clause_rela',
    },
    {
        'name': 'cltype_simp',
        'df': niv_df,