Fedora Localization Trends Analysis¶
This notebook analyzes translation trends across ALL languages in Fedora releases (f35, f40, f43).
Time Model: Fedora releases are treated as ordered time points (release-based time).
Release dates are included for clarity and future extensibility:
- f35: 2021-11-02
- f40: 2024-04-23
- f43: 2025-10-28
Using release-based time better reflects translation effort cadence, as localization work is tied to Fedora release cycles rather than calendar years.
Current trend calculations remain release-based comparisons (no per-year or per-month rates).
Analysis Scope¶
For each language, we compute three independent trend metrics:
- Translated Words Trend: Growth in absolute translated words
- Progress Trend: Language-level completion ratio (language sum / distribution total)
- Distribution Progress Trend: Fedora-wide contribution (language sum / distribution.csv total)
A positive trend is defined as: value(f43) > value(f35)
Configuration and Imports¶
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import os
import glob
In [3]:
# Configuration
RELEASES = ["f7","f8", "f9", "f10", "f11", "f12", "f13", "f14", "f15", "f16","f17","f18","f19", "f20", "f21", "f22", "f23", "f24", "f25", "f26", "f27", "f28", "f29", "f30", "f31", "f32", "f33", "f34", "f35", "f36", "f37", "f38", "f39", "f40", "f41", "f42", "f43"]
BASE_PATH = "../generated"
In [4]:
# Automatic language discovery from f43
# Scan stats/f43/ and treat each *.csv (except distribution.csv) as a language
all_languages = set()
discovery_release = "f43" # Use latest release for discovery
discovery_path = f"{BASE_PATH}/{discovery_release}/languages/"
if os.path.exists(discovery_path):
csv_files = glob.glob("*.csv", root_dir=f"{discovery_path}")
for csv_file in csv_files:
filename = os.path.basename(csv_file)
# Extract language code from filename (e.g., "gu.csv" -> "gu")
lang_code = filename.replace(".csv", "")
all_languages.add(lang_code)
all_languages = sorted(all_languages)
print(f"Discovered {len(all_languages)} languages: {all_languages}")
Discovered 345 languages: ['aa', 'ab', 'ace', 'ach', 'ae', 'af', 'ain', 'ak', 'aln', 'am', 'an', 'ang', 'anp', 'ar', 'ar_DZ', 'ar_EG', 'ar_LY', 'ar_MA', 'ar_SA', 'as', 'ast', 'av', 'ay', 'ayc', 'az', 'azb', 'ba', 'bal', 'ban', 'bar', 'be', 'be_Latn', 'bem', 'ber', 'bg', 'bh', 'bi', 'bin', 'bm', 'bn', 'bn_BD', 'bn_IN', 'bo', 'bqi', 'br', 'brx', 'bs', 'byn', 'ca', 'ca@valencia', 'cak', 'ce', 'cgg', 'ch', 'chr', 'ckb', 'ckb_IR', 'cnr', 'co', 'cpf', 'cpp', 'cr', 'crh', 'cs', 'csb', 'cu', 'cv', 'cy', 'da', 'de', 'de_AT', 'de_CH', 'doi', 'dsb', 'dv', 'dz', 'ee', 'el', 'en', 'en@pirate', 'en_AU', 'en_CA', 'en_GB', 'en_IE', 'en_IN', 'en_NZ', 'en_Shaw', 'en_US', 'en_ZA', 'enm', 'eo', 'error', 'es', 'es_419', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_US', 'es_UY', 'es_VE', 'et', 'eu', 'fa', 'ff', 'fi', 'fil', 'fj', 'fo', 'fr', 'fr_BE', 'fr_CA', 'fr_CH', 'frp', 'fur', 'fy', 'ga', 'gaa', 'gd', 'gez', 'gl', 'gn', 'grc', 'gu', 'guc', 'gug', 'gv', 'ha', 'haw', 'he', 'he_IL', 'hi', 'hne', 'ho', 'hr', 'hsb', 'ht', 'hu', 'hus', 'hy', 'hz', 'ia', 'id', 'ie', 'ig', 'ii', 'ik', 'ilo', 'io', 'is', 'isv', 'it', 'iu', 'ja', 'jam', 'jbo', 'jv', 'ka', 'kab', 'kg', 'ki', 'kj', 'kk', 'kl', 'km', 'kmr', 'kmr_Latn', 'kn', 'ko', 'kok', 'kos', 'kr', 'krl', 'ks', 'ku', 'kv', 'kw', 'ky', 'la', 'lb', 'lfn', 'lg', 'li', 'lmo', 'ln', 'lo', 'lt', 'ltg', 'lu', 'lv', 'lzh', 'mai', 'man', 'mg', 'mh', 'mhr', 'mi', 'mjw', 'mk', 'ml', 'mn', 'mni', 'mnk', 'mnw', 'mr', 'mrh', 'ms', 'ms_Arab', 'mt', 'mus', 'my', 'na', 'nah', 'nan_Hant', 'nap', 'nb_NO', 'nd', 'nds', 'ne', 'ng', 'nl', 'nl_BE', 'nn', 'nqo', 'nr', 'nso', 'nv', 'ny', 'oc', 'oj', 'om', 'or', 'os', 'pa', 'pa_PK', 'pam', 'pap', 'pi', 'pl', 'pms', 'prs', 'ps', 'pt', 'pt_BR', 'pt_PT', 'qu', 'rm', 'rn', 'ro', 'ro_MD', 'rom', 'ru', 'ru_UA', 'rue', 'rw', 'sa', 'sah', 'sat', 'sc', 'sco', 'sd', 'se', 'sg', 'sgs', 'shn', 'si', 'sid', 'sk', 'sl', 'sm', 'sma', 'smj', 'sn', 'so', 'son', 'sq', 'sr', 'sr@ijekavian', 'sr@ijekavian_Latn', 'sr_Cyrl', 'sr_Latn', 'ss', 'st', 'su', 'sv', 'sw', 'sw_TZ', 'szl', 'ta', 'te', 'tet', 'tg', 'th', 'ti', 'tig', 'tk', 'tl', 'tlh', 'tn', 'to', 'tok', 'tpi', 'tr', 'ts', 'tt', 'tt@iqtelif', 'tvl', 'tw', 'ty', 'tyv', 'tzm', 'udm', 'ug', 'uk', 'ur', 'ur_PK', 'uz', 'uz_Latn', 've', 'vec', 'vi', 'vo', 'wa', 'wae', 'wal', 'wo', 'xh', 'yi', 'yo', 'yue_Hant', 'za', 'zgh', 'zh_Hans', 'zh_Hans_SG', 'zh_Hant', 'zh_Hant_HK', 'zu']
In [5]:
# Load distribution data for all releases
# This provides totalsourcewords for computing distribution-wide progress
# Also includes release_date metadata for chronological ordering
distribution_data = {}
release_dates = {}
for release in RELEASES:
dist_path = f"{BASE_PATH}/{release}/release.csv"
if os.path.exists(dist_path):
df = pd.read_csv(dist_path)
distribution_data[release] = df
# Extract release_date if available
if "release_date" in df.columns:
release_dates[release] = df["release_date"].iloc[0]
else:
print(f"Warning: File not found: {dist_path}")
print(f"Loaded distribution data for: {list(distribution_data.keys())}")
print(f"Release dates: {release_dates}")
Loaded distribution data for: ['f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24', 'f25', 'f26', 'f27', 'f28', 'f29', 'f30', 'f31', 'f32', 'f33', 'f34', 'f35', 'f36', 'f37', 'f38', 'f39', 'f40', 'f41', 'f42', 'f43']
Release dates: {'f7': '2007-05-31 ', 'f8': '2007-11-08 ', 'f9': '2008-05-13 ', 'f10': '2008-11-25 ', 'f11': '2009-06-09 ', 'f12': '2009-11-17 ', 'f13': '2010-05-25 ', 'f14': '2010-11-02 ', 'f15': '2011-05-24 ', 'f16': '2011-11-08 ', 'f17': '2012-05-29 ', 'f18': '2013-01-15 ', 'f19': '2013-07-02 ', 'f20': '2013-12-17 ', 'f21': '2014-12-09 ', 'f22': '2015-05-26 ', 'f23': '2015-11-03 ', 'f24': '2016-06-21 ', 'f25': '2016-11-22 ', 'f26': '2017-07-11 ', 'f27': '2017-11-14 ', 'f28': '2018-05-01 ', 'f29': '2018-10-30 ', 'f30': '2019-04-30 ', 'f31': '2019-10-29 ', 'f32': '2020-04-28 ', 'f33': '2020-10-27 ', 'f34': '2021-04-27 ', 'f35': '2021-11-02 ', 'f36': '2022-05-10 ', 'f37': '2022-11-15 ', 'f38': '2023-04-18 ', 'f39': '2023-11-07', 'f40': '2024-04-23', 'f41': '2024-10-29', 'f42': '2025-04-15', 'f43': '2025-10-28'}
In [6]:
# Create release timeline DataFrame (for documentation and chronological sorting)
# Ensures releases are analyzed in chronological order
release_timeline = pd.DataFrame([
{"release": rel, "release_date": release_dates.get(rel, "Unknown")}
for rel in RELEASES
])
# Sort by date to confirm chronological order
if all(release_timeline["release_date"] != "Unknown"):
release_timeline["date_parsed"] = pd.to_datetime(release_timeline["release_date"], format='mixed')
release_timeline = release_timeline.sort_values("date_parsed")
RELEASES_SORTED = release_timeline["release"].tolist()
print(f"Chronologically sorted releases: {RELEASES_SORTED}")
else:
RELEASES_SORTED = RELEASES
print(f"Using default release order: {RELEASES_SORTED}")
release_timeline[["release", "release_date"]]
Chronologically sorted releases: ['f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24', 'f25', 'f26', 'f27', 'f28', 'f29', 'f30', 'f31', 'f32', 'f33', 'f34', 'f35', 'f36', 'f37', 'f38', 'f39', 'f40', 'f41', 'f42', 'f43']
Out[6]:
| release | release_date | |
|---|---|---|
| 0 | f7 | 2007-05-31 |
| 1 | f8 | 2007-11-08 |
| 2 | f9 | 2008-05-13 |
| 3 | f10 | 2008-11-25 |
| 4 | f11 | 2009-06-09 |
| 5 | f12 | 2009-11-17 |
| 6 | f13 | 2010-05-25 |
| 7 | f14 | 2010-11-02 |
| 8 | f15 | 2011-05-24 |
| 9 | f16 | 2011-11-08 |
| 10 | f17 | 2012-05-29 |
| 11 | f18 | 2013-01-15 |
| 12 | f19 | 2013-07-02 |
| 13 | f20 | 2013-12-17 |
| 14 | f21 | 2014-12-09 |
| 15 | f22 | 2015-05-26 |
| 16 | f23 | 2015-11-03 |
| 17 | f24 | 2016-06-21 |
| 18 | f25 | 2016-11-22 |
| 19 | f26 | 2017-07-11 |
| 20 | f27 | 2017-11-14 |
| 21 | f28 | 2018-05-01 |
| 22 | f29 | 2018-10-30 |
| 23 | f30 | 2019-04-30 |
| 24 | f31 | 2019-10-29 |
| 25 | f32 | 2020-04-28 |
| 26 | f33 | 2020-10-27 |
| 27 | f34 | 2021-04-27 |
| 28 | f35 | 2021-11-02 |
| 29 | f36 | 2022-05-10 |
| 30 | f37 | 2022-11-15 |
| 31 | f38 | 2023-04-18 |
| 32 | f39 | 2023-11-07 |
| 33 | f40 | 2024-04-23 |
| 34 | f41 | 2024-10-29 |
| 35 | f42 | 2025-04-15 |
| 36 | f43 | 2025-10-28 |
In [8]:
# Load language data for all discovered languages across all releases
# Structure: language_data[(release, lang)] = DataFrame
language_data = {}
for lang in all_languages:
for release in RELEASES:
csv_path = f"{BASE_PATH}/{release}/languages/{lang}.csv"
if os.path.exists(csv_path):
language_data[(release, lang)] = pd.read_csv(csv_path)
print(f"Loaded {len(language_data)} language-release combinations")
Loaded 10758 language-release combinations
In [9]:
# Compute metrics for all languages across all releases
# For each (language, release), compute:
# 1. translated_words: sum of translatedsourcewords column
# 2. language_total_words: sum of totalsourcewordssum column (for progress trend)
# 3. distribution_total_words: from distribution.csv (for distribution progress trend)
metrics = []
for (release, lang), df in language_data.items():
# Metric 1: Translated words (absolute)
translated_words = df["translatedsourcewords"].sum()
# Metric 2: Language-level total words (for language progress)
language_total_words = df["totalsourcewordssum"].sum()
# Metric 3: Distribution-wide total words (from distribution.csv)
if release in distribution_data:
distribution_total_words = distribution_data[release]["totalsourcewords"].iloc[0]
else:
distribution_total_words = None
# Compute progress ratios
language_progress = (translated_words / language_total_words * 100) if language_total_words > 0 else 0
distribution_progress = (translated_words / distribution_total_words * 100) if distribution_total_words else 0
metrics.append({
"language": lang,
"release": release,
"translated_words": translated_words,
"language_total_words": language_total_words,
"distribution_total_words": distribution_total_words,
"language_progress": language_progress,
"distribution_progress": distribution_progress
})
metrics_df = pd.DataFrame(metrics)
print(f"Computed metrics for {len(metrics_df)} language-release combinations")
metrics_df.head(15)
Computed metrics for 10758 language-release combinations
Out[9]:
| language | release | translated_words | language_total_words | distribution_total_words | language_progress | distribution_progress | |
|---|---|---|---|---|---|---|---|
| 0 | aa | f7 | 4 | 12020 | 3457836 | 0.033278 | 0.000116 |
| 1 | aa | f8 | 4 | 12174 | 3855649 | 0.032857 | 0.000104 |
| 2 | aa | f16 | 0 | 7138 | 9698971 | 0.000000 | 0.000000 |
| 3 | aa | f17 | 0 | 9286 | 10091855 | 0.000000 | 0.000000 |
| 4 | aa | f18 | 0 | 11181 | 10770053 | 0.000000 | 0.000000 |
| 5 | aa | f19 | 0 | 11181 | 11433072 | 0.000000 | 0.000000 |
| 6 | aa | f20 | 0 | 11181 | 12425872 | 0.000000 | 0.000000 |
| 7 | aa | f21 | 0 | 15825 | 13043139 | 0.000000 | 0.000000 |
| 8 | aa | f22 | 0 | 15825 | 13454942 | 0.000000 | 0.000000 |
| 9 | aa | f23 | 0 | 17168 | 13805956 | 0.000000 | 0.000000 |
| 10 | aa | f24 | 0 | 23532 | 14211507 | 0.000000 | 0.000000 |
| 11 | aa | f25 | 0 | 34546 | 14474492 | 0.000000 | 0.000000 |
| 12 | aa | f26 | 0 | 37264 | 14366991 | 0.000000 | 0.000000 |
| 13 | aa | f27 | 0 | 40994 | 14971951 | 0.000000 | 0.000000 |
| 14 | aa | f28 | 0 | 41906 | 15305875 | 0.000000 | 0.000000 |
In [10]:
# Pivot data to have languages as rows and releases as columns
# Create separate pivots for each metric
translated_words_pivot = metrics_df.pivot(
index="language",
columns="release",
values="translated_words"
).reset_index()
language_progress_pivot = metrics_df.pivot(
index="language",
columns="release",
values="language_progress"
).reset_index()
distribution_progress_pivot = metrics_df.pivot(
index="language",
columns="release",
values="distribution_progress"
).reset_index()
print("Pivoted data for all three metrics")
translated_words_pivot.head()
Pivoted data for all three metrics
Out[10]:
| release | language | f10 | f11 | f12 | f13 | f14 | f15 | f16 | f17 | f18 | ... | f37 | f38 | f39 | f40 | f41 | f42 | f43 | f7 | f8 | f9 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | aa | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 237.0 | 237.0 | 237.0 | 195.0 | 237.0 | 237.0 | 237.0 | 4.0 | 4.0 | NaN |
| 1 | ab | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 3.0 | 3.0 | ... | 60030.0 | 77127.0 | 102972.0 | 112846.0 | 120776.0 | 140583.0 | 143032.0 | NaN | NaN | NaN |
| 2 | ace | 412.0 | 443.0 | 405.0 | 405.0 | 405.0 | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN |
| 3 | ach | NaN | NaN | 2260.0 | 2424.0 | 2424.0 | 4042.0 | 4042.0 | 4042.0 | 4042.0 | ... | 12334.0 | 10806.0 | 12970.0 | 8819.0 | 12757.0 | 17404.0 | 13257.0 | NaN | NaN | NaN |
| 4 | ae | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN |
5 rows × 38 columns
In [11]:
# Compute positive trends for each metric
# Positive trend defined as: value(f43) > value(f35)
# Ensure f35 and f43 columns exist
if "f35" in translated_words_pivot.columns and "f43" in translated_words_pivot.columns:
translated_words_pivot["positive_trend"] = translated_words_pivot["f43"] > translated_words_pivot["f35"]
else:
print("Warning: Cannot compute translated words trend - missing f35 or f43 data")
translated_words_pivot["positive_trend"] = False
if "f35" in language_progress_pivot.columns and "f43" in language_progress_pivot.columns:
language_progress_pivot["positive_trend"] = language_progress_pivot["f43"] > language_progress_pivot["f35"]
else:
print("Warning: Cannot compute language progress trend - missing f35 or f43 data")
language_progress_pivot["positive_trend"] = False
if "f35" in distribution_progress_pivot.columns and "f43" in distribution_progress_pivot.columns:
distribution_progress_pivot["positive_trend"] = distribution_progress_pivot["f43"] > distribution_progress_pivot["f35"]
else:
print("Warning: Cannot compute distribution progress trend - missing f35 or f43 data")
distribution_progress_pivot["positive_trend"] = False
print("Computed positive trends for all three metrics")
translated_words_pivot.head()
Computed positive trends for all three metrics
Out[11]:
| release | language | f10 | f11 | f12 | f13 | f14 | f15 | f16 | f17 | f18 | ... | f38 | f39 | f40 | f41 | f42 | f43 | f7 | f8 | f9 | positive_trend |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | aa | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 237.0 | 237.0 | 195.0 | 237.0 | 237.0 | 237.0 | 4.0 | 4.0 | NaN | True |
| 1 | ab | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 3.0 | 3.0 | ... | 77127.0 | 102972.0 | 112846.0 | 120776.0 | 140583.0 | 143032.0 | NaN | NaN | NaN | True |
| 2 | ace | 412.0 | 443.0 | 405.0 | 405.0 | 405.0 | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 3 | ach | NaN | NaN | 2260.0 | 2424.0 | 2424.0 | 4042.0 | 4042.0 | 4042.0 | 4042.0 | ... | 10806.0 | 12970.0 | 8819.0 | 12757.0 | 17404.0 | 13257.0 | NaN | NaN | NaN | True |
| 4 | ae | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
5 rows × 39 columns
In [12]:
# Compute summary statistics for the four bars
# Filter to only include languages with data for both f35 and f43
# Bar 1: Total number of languages in f43
total_languages = len(all_languages)
# Bar 2: Languages with positive translated words trend
translated_valid = translated_words_pivot.dropna(subset=["f35", "f43"])
positive_translated_words = translated_valid["positive_trend"].sum()
# Bar 3: Languages with positive language progress trend
progress_valid = language_progress_pivot.dropna(subset=["f35", "f43"])
positive_language_progress = progress_valid["positive_trend"].sum()
# Bar 4: Languages with positive distribution progress trend
dist_progress_valid = distribution_progress_pivot.dropna(subset=["f35", "f43"])
positive_distribution_progress = dist_progress_valid["positive_trend"].sum()
print(f"Total languages in f43: {total_languages}")
print(f"Languages with positive translated words trend: {positive_translated_words}")
print(f"Languages with positive language progress trend: {positive_language_progress}")
print(f"Languages with positive distribution progress trend: {positive_distribution_progress}")
Total languages in f43: 345 Languages with positive translated words trend: 174 Languages with positive language progress trend: 138 Languages with positive distribution progress trend: 90
In [13]:
# Create vertical bar chart with four bars
# Use hatch patterns for accessibility (no colors)
fig, ax = plt.subplots(figsize=(10, 6))
categories = [
"Total Languages\n(f43)",
"Positive\nTranslated Words\nTrend",
"Positive\nLanguage Progress\nTrend",
"Positive\nDistribution Progress\nTrend"
]
values = [
total_languages,
positive_translated_words,
positive_language_progress,
positive_distribution_progress
]
# Define hatch patterns for each bar
hatches = ['', '///', '\\\\\\', 'xxx']
bars = ax.bar(categories, values, color='white', edgecolor='black', linewidth=1.5)
# Apply different hatch patterns to each bar
for bar, hatch in zip(bars, hatches):
bar.set_hatch(hatch)
# Add value labels on top of each bar
for i, (cat, val) in enumerate(zip(categories, values)):
ax.text(i, val + 0.1, str(int(val)), ha='center', va='bottom', fontsize=11, fontweight='bold')
# Build title with release dates if available
if release_dates:
date_range = f"{release_dates.get('f35', 'f35')} → {release_dates.get('f43', 'f43')}"
title = f"Fedora Localization Trends Summary\n(f35 → f43: {date_range})"
else:
title = "Fedora Localization Trends Summary (f35 → f43)"
ax.set_ylabel("Number of Languages", fontsize=12)
ax.set_title(title, fontsize=14, fontweight='bold')
ax.set_ylim(0, max(values) * 1.15)
ax.grid(axis='y', alpha=0.3, linestyle='--')
plt.tight_layout()
plt.show()
In [14]:
# View translated words evolution for all languages
translated_words_pivot.sort_values(by="f43", ascending=False)
Out[14]:
| release | language | f10 | f11 | f12 | f13 | f14 | f15 | f16 | f17 | f18 | ... | f38 | f39 | f40 | f41 | f42 | f43 | f7 | f8 | f9 | positive_trend |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | fr | 3278761.0 | 3480201.0 | 3606670.0 | 3792313.0 | 3993891.0 | 4126640.0 | 4971948.0 | 5526147.0 | 5814230.0 | ... | 11353172.0 | 11942937.0 | 12010992.0 | 12834621.0 | 13026996.0 | 12721173.0 | 2381499.0 | 2435615.0 | 2652440.0 | True |
| 69 | de | 3012530.0 | 3278903.0 | 3408173.0 | 3567481.0 | 3749889.0 | 3886767.0 | 4766742.0 | 5267544.0 | 5589272.0 | ... | 10931303.0 | 11424786.0 | 11394370.0 | 11757061.0 | 11925119.0 | 11571153.0 | 2071483.0 | 2104876.0 | 2347071.0 | True |
| 92 | es | 3105932.0 | 3372876.0 | 3531746.0 | 3752970.0 | 3970812.0 | 4163082.0 | 5156558.0 | 5631163.0 | 6030101.0 | ... | 10681656.0 | 10951224.0 | 10979677.0 | 11468045.0 | 11679167.0 | 10718286.0 | 2193043.0 | 2242024.0 | 2458943.0 | True |
| 259 | ru | 2137237.0 | 2271978.0 | 2395917.0 | 2585563.0 | 2771261.0 | 2987452.0 | 3721363.0 | 4125968.0 | 4378951.0 | ... | 9284698.0 | 9758274.0 | 9696281.0 | 10992820.0 | 11052834.0 | 10378786.0 | 1525515.0 | 1491938.0 | 1699461.0 | True |
| 321 | uk | 1045239.0 | 1191644.0 | 1324943.0 | 1494693.0 | 1633798.0 | 1693106.0 | 2150781.0 | 2391505.0 | 2615061.0 | ... | 7467192.0 | 8058446.0 | 8333624.0 | 9119700.0 | 9187197.0 | 8758076.0 | 731533.0 | 718277.0 | 783633.0 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43 | bqi | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 316 | ty | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 35 | bh | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 4 | ae | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 2 | ace | 412.0 | 443.0 | 405.0 | 405.0 | 405.0 | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
345 rows × 39 columns
In [15]:
# View language progress evolution (language-level completion %)
language_progress_pivot.sort_values(by="f43", ascending=False)
Out[15]:
| release | language | f10 | f11 | f12 | f13 | f14 | f15 | f16 | f17 | f18 | ... | f38 | f39 | f40 | f41 | f42 | f43 | f7 | f8 | f9 | positive_trend |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 271 | sgs | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 100.000000 | 99.828375 | 100.0 | 100.0 | 100.0 | 100.0 | NaN | NaN | NaN | False |
| 57 | cnr | NaN | NaN | NaN | NaN | NaN | NaN | 100.0 | 100.0 | 100.0 | ... | 0.360079 | 100.000000 | 100.0 | 100.0 | 100.0 | 100.0 | NaN | NaN | NaN | True |
| 132 | guc | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 100.000000 | 100.000000 | 100.0 | 100.0 | 100.0 | 100.0 | NaN | NaN | NaN | False |
| 158 | isv | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 100.000000 | 100.000000 | 100.0 | 100.0 | 100.0 | 100.0 | NaN | NaN | NaN | False |
| 84 | en_IN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 100.000000 | 100.000000 | 100.0 | 100.0 | 100.0 | 100.0 | NaN | NaN | NaN | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 65 | cu | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 316 | ty | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 35 | bh | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 4 | ae | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
| 2 | ace | 19.153882 | 20.995261 | 19.669743 | 19.669743 | 19.669743 | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | False |
345 rows × 39 columns
In [16]:
# View distribution progress evolution (Fedora-wide contribution %)
distribution_progress_pivot.sort_values(by="f43", ascending=False)
Out[16]:
| release | language | f10 | f11 | f12 | f13 | f14 | f15 | f16 | f17 | f18 | ... | f38 | f39 | f40 | f41 | f42 | f43 | f7 | f8 | f9 | positive_trend |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | fr | 61.265292 | 60.504074 | 57.133991 | 37.539265 | 38.130470 | 37.554539 | 51.262634 | 54.758486 | 53.985157 | ... | 55.962471 | 57.216273 | 57.669823 | 59.168176 | 59.602841 | 60.159342 | 68.872526 | 63.170040 | 64.647696 | True |
| 69 | de | 56.290633 | 57.004463 | 53.989559 | 35.313703 | 35.800934 | 35.371572 | 49.146884 | 52.195994 | 51.896421 | ... | 53.882979 | 54.733913 | 54.709161 | 54.200576 | 54.561387 | 54.720815 | 59.906919 | 54.592003 | 57.204963 | False |
| 92 | es | 58.035896 | 58.638205 | 55.947104 | 37.149817 | 37.910130 | 37.886180 | 53.166032 | 55.799087 | 55.989520 | ... | 52.652410 | 52.465171 | 52.718046 | 52.868199 | 53.436075 | 50.687546 | 63.422412 | 58.149069 | 59.931610 | False |
| 259 | ru | 39.935344 | 39.498847 | 37.954207 | 25.593914 | 26.457778 | 27.187345 | 38.368637 | 40.884139 | 40.658584 | ... | 45.766473 | 46.749981 | 46.555922 | 50.677391 | 50.570393 | 49.082026 | 44.117622 | 38.694860 | 41.420819 | True |
| 321 | uk | 19.530815 | 20.716998 | 20.988691 | 14.795634 | 15.598194 | 15.408133 | 22.175352 | 23.697378 | 24.280855 | ... | 36.807556 | 38.606437 | 40.013233 | 42.042224 | 42.034483 | 41.417571 | 21.155804 | 18.629211 | 19.099421 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 43 | bqi | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | False |
| 316 | ty | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | False |
| 35 | bh | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | False |
| 4 | ae | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | False |
| 2 | ace | 0.007698 | 0.007702 | 0.006416 | 0.004009 | 0.003867 | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | False |
345 rows × 39 columns
Additional Visualizations¶
Simple line plots showing metric evolution for top languages.
In [22]:
# Line plot: Translated words trend for top 5 languages (by f43 volume)
top_5_langs = translated_words_pivot.nlargest(5, "f43")
fig, ax = plt.subplots(figsize=(10, 6))
for idx, row in top_5_langs.iterrows():
lang = row["language"]
values = [row[rel] for rel in RELEASES if rel in row.index and pd.notna(row[rel])]
releases = [rel for rel in RELEASES if rel in row.index and pd.notna(row[rel])]
ax.plot(releases, values, marker='o', label=lang, linewidth=2)
ax.set_xlabel("Fedora Release", fontsize=12)
ax.set_ylabel("Translated Words", fontsize=12)
ax.set_title("Translated Words Trend - Top 5 Languages", fontsize=14, fontweight='bold')
ax.legend(title="Language", fontsize=10)
ax.grid(True, alpha=0.3, linestyle='--')
plt.tight_layout()
plt.show()
In [23]:
# Horizontal bar chart: Languages ranked by f43 translated words
# Show all languages with hatching for accessibility
fig, ax = plt.subplots(figsize=(10, 8))
sorted_data = translated_words_pivot.sort_values(by="f43", ascending=True)
languages = sorted_data["language"].tolist()
f43_values = sorted_data["f43"].tolist()
bars = ax.barh(languages, f43_values, color='white', edgecolor='black', linewidth=1.2)
# Apply hatch pattern
for bar in bars:
bar.set_hatch('//')
ax.set_xlabel("Translated Words in f43", fontsize=12)
ax.set_ylabel("Language", fontsize=12)
ax.set_title("Languages Ranked by Translated Words (f43)", fontsize=14, fontweight='bold')
ax.grid(axis='x', alpha=0.3, linestyle='--')
plt.tight_layout()
plt.show()
Insights Summary¶
Key findings from the trend analysis across f35 → f40 → f43.
In [24]:
# Print detailed summary statistics
print("=" * 60)
print("FEDORA LOCALIZATION TRENDS SUMMARY (f35 → f43)")
print("=" * 60)
print()
print(f"Total languages analyzed: {total_languages}")
print()
print("TREND ANALYSIS:")
print(f" • Languages with positive translated words trend: {positive_translated_words}")
print(f" (Growth in absolute translated words)")
print()
print(f" • Languages with positive language progress trend: {positive_language_progress}")
print(f" (Growth in language-level completion ratio)")
print()
print(f" • Languages with positive distribution progress trend: {positive_distribution_progress}")
print(f" (Growth in Fedora-wide contribution percentage)")
print()
print("=" * 60)
print()
print("Top 3 languages by translated words in f43:")
top_3 = translated_words_pivot.nlargest(3, "f43")[["language", "f43"]]
for idx, row in top_3.iterrows():
print(f" {row['language']}: {int(row['f43']):,} words")
print()
print("=" * 60)
============================================================
FEDORA LOCALIZATION TRENDS SUMMARY (f35 → f43)
============================================================
Total languages analyzed: 345
TREND ANALYSIS:
• Languages with positive translated words trend: 174
(Growth in absolute translated words)
• Languages with positive language progress trend: 138
(Growth in language-level completion ratio)
• Languages with positive distribution progress trend: 90
(Growth in Fedora-wide contribution percentage)
============================================================
Top 3 languages by translated words in f43:
fr: 12,721,173 words
de: 11,571,153 words
es: 10,718,286 words
============================================================