Fix the CPI CSV and remove twitter functionalities (#5350)

* remove twitter functions

* remove twitter functions and all references to it

* remove twitter functions

* remove twitter functions and all references to it

* Fix units on cpi

* crlf - lf

* missed one

* update ba menu integration test

---------

Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com>

22 files changed, 259 insertions, 1025 deletions

diff --git a/openbb_terminal/common/behavioural_analysis/twitter_model.py b/openbb_terminal/common/behavioural_analysis/twitter_model.py
deleted file mode 100644
index 034cc834113..00000000000
--- a/openbb_terminal/common/behavioural_analysis/twitter_model.py
+++ /dev/null
@@ -1,212 +0,0 @@
-"""Twitter Model"""
-__docformat__ = "numpy"
-
-import logging
-from datetime import datetime, timedelta
-from typing import Optional
-
-import pandas as pd
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-from openbb_terminal.core.session.current_user import get_current_user
-from openbb_terminal.decorators import check_api_key, log_start_end
-from openbb_terminal.helper_funcs import clean_tweet, get_data, request
-from openbb_terminal.rich_config import console
-
-logger = logging.getLogger(__name__)
-
-analyzer = SentimentIntensityAnalyzer()
-
-
-@log_start_end(log=logger)
-@check_api_key(["API_TWITTER_BEARER_TOKEN"])
-def load_analyze_tweets(
-    symbol: str,
-    limit: int = 100,
-    start_date: Optional[str] = "",
-    end_date: Optional[str] = "",
-) -> pd.DataFrame:
-    """Load tweets from twitter API and analyzes using VADER.
-
-    Parameters
-    ----------
-    symbol: str
-        Ticker symbol to search twitter for
-    limit: int
-        Number of tweets to analyze
-    start_date: Optional[str]
-        If given, the start time to get tweets from
-    end_date: Optional[str]
-        If given, the end time to get tweets from
-
-    Returns
-    -------
-    df_tweet: pd.DataFrame
-        Dataframe of tweets and sentiment
-    """
-    params = {
-        "query": rf"(\${symbol}) (lang:en)",
-        "max_results": str(limit),
-        "tweet.fields": "created_at,lang",
-    }
-
-    if start_date:
-        # Assign from and to datetime parameters for the API
-        params["start_time"] = start_date
-    if end_date:
-        params["end_time"] = end_date
-
-    # Request Twitter API
-    response = request(
-        "https://api.twitter.com/2/tweets/search/recent",
-        params=params,  # type: ignore
-        headers={
-            "authorization": "Bearer "
-            + get_current_user().credentials.API_TWITTER_BEARER_TOKEN
-        },
-    )
-
-    # Create dataframe
-    df_tweets = pd.DataFrame()
-
-    # Check that the API response was successful
-    if response.status_code == 200:
-        tweets = []
-        for tweet in response.json()["data"]:
-            row = get_data(tweet)
-            tweets.append(row)
-        df_tweets = pd.DataFrame(tweets)
-    elif response.status_code == 401:
-        console.print("Twitter API Key provided is incorrect\n")
-        return pd.DataFrame()
-    elif response.status_code == 400:
-        console.print(
-            """
-            Status Code 400.
-            This means you are requesting data from beyond the API's 7 day limit"""
-        )
-        return pd.DataFrame()
-    elif response.status_code == 403:
-        console.print(
-            f"""
-            Status code 403.
-            It seems you're twitter credentials are invalid - {response.text}
-        """
-        )
-        return pd.DataFrame()
-    else:
-        console.print(
-            f"""
-            Status code {response.status_code}.
-            Something went wrong - {response.text}
-        """
-        )
-        return pd.DataFrame()
-
-    sentiments = []
-    pos = []
-    neg = []
-    neu = []
-
-    for s_tweet in df_tweets["text"].to_list():
-        tweet = clean_tweet(s_tweet, symbol)
-        sentiments.append(analyzer.polarity_scores(tweet)["compound"])
-        pos.append(analyzer.polarity_scores(tweet)["pos"])
-        neg.append(analyzer.polarity_scores(tweet)["neg"])
-        neu.append(analyzer.polarity_scores(tweet)["neu"])
-    # Add sentiments to tweets dataframe
-    df_tweets["sentiment"] = sentiments
-    df_tweets["positive"] = pos
-    df_tweets["negative"] = neg
-    df_tweets["neutral"] = neu
-
-    return df_tweets
-
-
-@log_start_end(log=logger)
-def get_sentiment(
-    symbol: str,
-    n_tweets: int = 15,
-    n_days_past: int = 2,
-) -> pd.DataFrame:
-    """Get sentiments from symbol.
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol to get sentiment for
-    n_tweets: int
-        Number of tweets to get per hour
-    n_days_past: int
-        Number of days to extract tweets for
-
-    Returns
-    -------
-    df_sentiment: pd.DataFrame
-        Dataframe of sentiment
-    """
-    # Date format string required by twitter
-    dt_format = "%Y-%m-%dT%H:%M:%SZ"
-
-    # Algorithm to extract
-    dt_recent = datetime.utcnow() - timedelta(seconds=20)
-    dt_old = dt_recent - timedelta(days=n_days_past)
-    console.print(
-        f"From {dt_recent.date()} retrieving {n_tweets*24} tweets ({n_tweets} tweets/hour)"
-    )
-
-    df_tweets = pd.DataFrame(
-        columns=[
-            "created_at",
-            "text",
-            "sentiment",
-            "positive",
-            "negative",
-            "neutral",
-        ]
-    )
-    while True:
-        # Iterate until we haven't passed the old number of days
-        if dt_recent < dt_old:
-            break
-        # Update past datetime
-        dt_past = dt_recent - timedelta(minutes=60)
-
-        temp = load_analyze_tweets(
-            symbol,
-            n_tweets,
-            start_date=dt_past.strftime(dt_format),
-            end_date=dt_recent.strftime(dt_format),
-        )
-
-        if (isinstance(temp, pd.DataFrame) and temp.empty) or (
-            not isinstance(temp, pd.DataFrame) and not temp
-        ):
-            return pd.DataFrame()
-
-        df_tweets = pd.concat([df_tweets, temp])
-
-        if dt_past.day < dt_recent.day:
-            console.print(
-                f"From {dt_past.date()} retrieving {n_tweets*24} tweets ({n_tweets} tweets/hour)"
-            )
-
-        # Update recent datetime
-        dt_recent = dt_past
-
-    # Sort tweets per date
-    df_tweets.sort_index(ascending=False, inplace=True)
-    df_tweets["cumulative_compound"] = df_tweets["sentiment"].cumsum()
-    df_tweets["prob_sen"] = 1
-
-    # df_tweets.to_csv(r'notebooks/tweets.csv', index=False)
-    df_tweets.reset_index(inplace=True)
-    df_tweets["Month"] = pd.to_datetime(df_tweets["created_at"]).apply(
-        lambda x: x.month
-    )
-    df_tweets["Day"] = pd.to_datetime(df_tweets["created_at"]).apply(lambda x: x.day)
-    df_tweets["date"] = pd.to_datetime(df_tweets["created_at"])
-    df_tweets = df_tweets.sort_values(by="date")
-    df_tweets["cumulative_compound"] = df_tweets["sentiment"].cumsum()
-
-    return df_tweets
diff --git a/openbb_terminal/common/behavioural_analysis/twitter_view.py b/openbb_terminal/common/behavioural_analysis/twitter_view.py
deleted file mode 100644
index fba72770628..00000000000
--- a/openbb_terminal/common/behavioural_analysis/twitter_view.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""Twitter view."""
-__docformat__ = "numpy"
-
-import logging
-import os
-from typing import Optional, Union
-
-import numpy as np
-import pandas as pd
-from dateutil import parser as dparse
-
-from openbb_terminal import OpenBBFigure, theme
-from openbb_terminal.common.behavioural_analysis import twitter_model
-from openbb_terminal.decorators import log_start_end
-from openbb_terminal.helper_funcs import export_data, get_closing_price
-from openbb_terminal.rich_config import console
-
-logger = logging.getLogger(__name__)
-
-
-@log_start_end(log=logger)
-def display_inference(
-    symbol: str, limit: int = 100, export: str = "", sheet_name: Optional[str] = None
-):
-    """Prints Inference sentiment from past n tweets.
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol
-    limit: int
-        Number of tweets to analyze
-    sheet_name: str
-        Optionally specify the name of the sheet the data is exported to.
-    export: str
-        Format to export tweet dataframe
-    """
-    df_tweets = twitter_model.load_analyze_tweets(symbol, limit)
-
-    if (isinstance(df_tweets, pd.DataFrame) and df_tweets.empty) or (
-        not isinstance(df_tweets, pd.DataFrame) and not df_tweets
-    ):
-        return
-
-    # Parse tweets
-    dt_from = dparse.parse(df_tweets["created_at"].values[-1])
-    dt_to = dparse.parse(df_tweets["created_at"].values[0])
-    console.print(f"From: {dt_from.strftime('%Y-%m-%d %H:%M:%S')}")
-    console.print(f"To:   {dt_to.strftime('%Y-%m-%d %H:%M:%S')}")
-
-    console.print(f"{len(df_tweets)} tweets were analyzed.")
-    dt_delta = dt_to - dt_from
-    n_freq = dt_delta.total_seconds() / len(df_tweets)
-    console.print(f"Frequency of approx 1 tweet every {round(n_freq)} seconds.")
-
-    pos = df_tweets["positive"]
-    neg = df_tweets["negative"]
-
-    percent_pos = len(np.where(pos > neg)[0]) / len(df_tweets)
-    percent_neg = len(np.where(pos < neg)[0]) / len(df_tweets)
-    total_sent = np.round(np.sum(df_tweets["sentiment"]), 2)
-    mean_sent = np.round(np.mean(df_tweets["sentiment"]), 2)
-    console.print(f"The summed compound sentiment of {symbol} is: {total_sent}")
-    console.print(f"The average compound sentiment of {symbol} is: {mean_sent}")
-    console.print(
-        f"Of the last {len(df_tweets)} tweets, {100*percent_pos:.2f} % had a higher positive sentiment"
-    )
-    console.print(
-        f"Of the last {len(df_tweets)} tweets, {100*percent_neg:.2f} % had a higher negative sentiment"
-    )
-
-    export_data(
-        export,
-        os.path.dirname(os.path.abspath(__file__)),
-        "infer",
-        df_tweets,
-        sheet_name,
-    )
-
-
-@log_start_end(log=logger)
-def display_sentiment(
-    symbol: str,
-    n_tweets: int = 15,
-    n_days_past: int = 2,
-    compare: bool = False,
-    export: str = "",
-    sheet_name: Optional[str] = None,
-    external_axes: bool = False,
-) -> Union[OpenBBFigure, None]:
-    """Plots sentiments from symbol
-
-    Parameters
-    ----------
-    symbol: str
-        Stock ticker symbol to get sentiment for
-    n_tweets: int
-        Number of tweets to get per hour
-    n_days_past: int
-        Number of days to extract tweets for
-    compare: bool
-        Show corresponding change in stock price
-    sheet_name: str
-        Optionally specify the name of the sheet the data is exported to.
-    export: str
-        Format to export tweet dataframe
-    external_axes: bool, optional
-        Whether to return the figure object or not, by default False
-    """
-
-    df_tweets = twitter_model.get_sentiment(symbol, n_tweets, n_days_past)
-
-    if df_tweets.empty:
-        return None
-
-    if compare:
-        plots_kwargs = dict(
-            rows=3,
-            cols=1,
-            shared_xaxes=True,
-            vertical_spacing=0.05,
-            row_heights=[0.2, 0.6, 0.2],
-        )
-    else:
-        plots_kwargs = dict(
-            rows=2,
-            cols=1,
-            shared_xaxes=True,
-            vertical_spacing=0.05,
-            row_heights=[0.6, 0.4],
-        )
-
-    fig = OpenBBFigure.create_subplots(**plots_kwargs)  # type: ignore
-
-    fig.add_scatter(
-        x=pd.to_datetime(df_tweets["created_at"]),
-        y=df_tweets["cumulative_compound"].values,
-        row=1,
-        col=1,
-    )
-
-    fig.set_yaxis_title("<br>Cumulative<br>VADER Sentiment", row=1, col=1)
-
-    for _, day_df in df_tweets.groupby(by="Day"):
-        day_df["time"] = pd.to_datetime(day_df["created_at"])
-        day_df = day_df.sort_values(by="time")
-        fig.add_scatter(
-            x=day_df["time"],
-            y=day_df["sentiment"].cumsum(),
-            row=1,
-            col=1,
-        )
-        fig.add_bar(
-            x=df_tweets["date"],
-            y=df_tweets["positive"],
-            row=2,
-            col=1,
-            marker_color=theme.up_color,
-        )
-
-    fig.add_bar(
-        x=df_tweets["date"],
-        y=-1 * df_tweets["negative"],
-        row=2,
-        col=1,
-        marker_color=theme.down_color,
-    )
-    fig.set_yaxis_title("VADER Polarity Scores", row=2, col=1)
-
-    if compare:
-        # get stock end price for each corresponding day if compare == True
-        closing_price_df = get_closing_price(symbol, n_days_past)
-        fig.add_scatter(
-            x=closing_price_df["Date"],
-            y=closing_price_df["Close"],
-            name=pd.to_datetime(closing_price_df["Date"]).iloc[0].strftime("%Y-%m-%d"),
-            row=3,
-            col=1,
-        )
-        fig.set_yaxis_title("Stock Price", row=3, col=1)
-
-    fig.update_layout(
-        title=f"Twitter's {symbol} total compound sentiment over time is {round(np.sum(df_tweets['sentiment']), 2)}",
-        xaxis=dict(type="date"),
-        showlegend=False,
-    )
-
-    export_data(
-        export,
-        os.path.dirname(os.path.abspath(__file__)),
-        "sentiment",
-        df_tweets,
-        sheet_name,
-        fig,
-    )
-
-    return fig.show(external=external_axes)
diff --git a/openbb_terminal/core/sdk/controllers/stocks_sdk_controller.py b/openbb_terminal/core/sdk/controllers/stocks_sdk_controller.py
index 676362d2e6e..4ddbb03f5a6 100644
--- a/openbb_terminal/core/sdk/controllers/stocks_sdk_controller.py
+++ b/openbb_terminal/core/sdk/controllers/stocks_sdk_controller.py
@@ -42,8 +42,6 @@ class StocksController(model.StocksRoot):
             `getdd`: Get due diligence posts from list of subreddits [Source: reddit].\n
             `headlines`: Gets Sentiment analysis provided by FinBrain's API [Source: finbrain].\n
             `headlines_chart`: Plots Sentiment analysis from FinBrain. Prints table if raw is True. [Source: FinBrain]\n
-            `infer`: Load tweets from twitter API and analyzes using VADER.\n
-            `infer_chart`: Prints Inference sentiment from past n tweets.\n
             `mentions`: Get interest over time from google api [Source: google].\n
             `mentions_chart`: Plots weekly bars of stock's interest over time. other users watchlist. [Source: Google].\n
             `messages`: Get last messages for a given ticker [Source: stocktwits].\n
@@ -55,8 +53,6 @@ class StocksController(model.StocksRoot):
             `regions`: Get interest by region from google api [Source: google].\n
             `regions_chart`: Plots bars of regions based on stock's interest. [Source: Google].\n
             `rise`: Get top rising related queries with this stock's query [Source: google].\n
-            `sentiment`: Get sentiments from symbol.\n
-            `sentiment_chart`: Plots sentiments from symbol\n
             `snews`: Get headlines sentiment using VADER model over time. [Source: Finnhub]\n
             `snews_chart`: Display stock price and headlines sentiment using VADER model over time. [Source: Finnhub]\n
             `stalker`: Gets messages from given user [Source: stocktwits].\n
diff --git a/openbb_terminal/core/sdk/models/keys_sdk_model.py b/openbb_terminal/core/sdk/models/keys_sdk_model.py
index 0572e6d3ded..0ccee257bd4 100644
--- a/openbb_terminal/core/sdk/models/keys_sdk_model.py
+++ b/openbb_terminal/core/sdk/models/keys_sdk_model.py
@@ -42,7 +42,6 @@ class KeysRoot(Category):
         `stocksera`: Set Stocksera key.\n
         `tokenterminal`: Set Token Terminal key.\n
         `tradier`: Set Tradier key\n
-        `twitter`: Set Twitter key\n
         `ultima`: Set Ultima Insights key\n
         `walert`: Set Walert key\n
     """
@@ -84,6 +83,5 @@ class KeysRoot(Category):
         self.stocksera = lib.keys_model.set_stocksera_key
         self.tokenterminal = lib.keys_model.set_tokenterminal_key
         self.tradier = lib.keys_model.set_tradier_key
-        self.twitter = lib.keys_model.set_twitter_key
         self.ultima = lib.keys_model.set_ultima_key
         self.walert = lib.keys_model.set_walert_key
diff --git a/openbb_terminal/core/sdk/models/stocks_sdk_model.py b/openbb_terminal/core/sdk/models/stocks_sdk_model.py
index f0e4c74c4ac..193caf164e9 100644
--- a/openbb_terminal/core/sdk/models/stocks_sdk_model.py
+++ b/openbb_terminal/core/sdk/models/stocks_sdk_model.py
@@ -42,8 +42,6 @@ class StocksBehavioralAnalysis(Category):
         `getdd`: Get due diligence posts from list of subreddits [Source: reddit].\n
         `headlines`: Gets Sentiment analysis provided by FinBrain's API [Source: finbrain].\n
         `headlines_chart`: Plots Sentiment analysis from FinBrain. Prints table if raw is True. [Source: FinBrain]\n
-        `infer`: Load tweets from twitter API and analyzes using VADER.\n
-        `infer_chart`: Prints Inference sentiment from past n tweets.\n
         `mentions`: Get interest over time from google api [Source: google].\n
         `mentions_chart`: Plots weekly bars of stock's interest over time. other users watchlist. [Source: Google].\n
         `messages`: Get last messages for a given ticker [Source: stocktwits].\n
@@ -55,8 +53,6 @@ class StocksBehavioralAnalysis(Category):
         `regions`: Get interest by region from google api [Source: google].\n
         `regions_chart`: Plots bars of regions based on stock's interest. [Source: Google].\n
         `rise`: Get top rising related queries with this stock's query [Source: google].\n
-        `sentiment`: Get sentiments from symbol.\n
-        `sentiment_chart`: Plots sentiments from symbol\n
         `snews`: Get headlines sentiment using VADER model over time. [Source: Finnhub]\n
         `snews_chart`: Display stock price and headlines sentiment using VADER model over time. [Source: Finnhub]\n
         `stalker`: Gets messages from given user [Source: stocktwits].\n
@@ -74,8 +70,6 @@ class StocksBehavioralAnalysis(Category):
         self.getdd = lib.stocks_ba_reddit_model.get_due_dilligence
         self.headlines = lib.stocks_ba_finbrain_model.get_sentiment
         self.headlines_chart = lib.stocks_ba_finbrain_view.display_sentiment_analysis
-        self.infer = lib.stocks_ba_twitter_model.load_analyze_tweets
-        self.infer_chart = lib.stocks_ba_twitter_view.display_inference
         self.mentions = lib.stocks_ba_google_model.get_mentions
         self.mentions_chart = lib.stocks_ba_google_view.display_mentions
         self.messages = lib.stocks_ba_stocktwits_model.get_messages
@@ -87,8 +81,6 @@ class StocksBehavioralAnalysis(Category):
         self.regions = lib.stocks_ba_google_model.get_regions
         self.regions_chart = lib.stocks_ba_google_view.display_regions
         self.rise = lib.stocks_ba_google_model.get_rise
-        self.sentiment = lib.stocks_ba_twitter_model.get_sentiment
-        self.sentiment_chart = lib.stocks_ba_twitter_view.display_sentiment
         self.snews = lib.stocks_ba_finnhub_model.get_headlines_sentiment
         self.snews_chart = (
             lib.stocks_ba_finnhub_view.display_stock_price_headlines_sentiment
diff --git a/openbb_terminal/core/sdk/sdk_init.py b/openbb_terminal/core/sdk/sdk_init.py
index 0ec4b436d87..66050033423 100644
--- a/openbb_terminal/core/sdk/sdk_init.py
+++ b/openbb_terminal/core/sdk/sdk_init.py
@@ -121,8 +121,6 @@ from openbb_terminal.common.behavioural_analysis import (
     reddit_view as stocks_ba_reddit_view,
     stocktwits_model as stocks_ba_stocktwits_model,
     stocktwits_view as stocks_ba_stocktwits_view,
-    twitter_model as stocks_ba_twitter_model,
-    twitter_view as stocks_ba_twitter_view,
 )
 
 
diff --git a/openbb_terminal/core/sdk/trail_map.csv b/openbb_terminal/core/sdk/trail_map.csv
index 33ff8830bff..1f62a953e00 100644
--- a/openbb_terminal/core/sdk/trail_map.csv
+++ b/openbb_terminal/core/sdk/trail_map.csv
@@ -312,7 +312,6 @@ keys.smartstake,keys_model.set_smartstake_key,
 keys.stocksera,keys_model.set_stocksera_key,
 keys.tokenterminal,keys_model.set_tokenterminal_key,
 keys.tradier,keys_model.set_tradier_key,
-keys.twitter,keys_model.set_twitter_key,
 keys.ultima,keys_model.set_ultima_key,
 keys.walert,keys_model.set_walert_key,
 login,sdk_session.login,
@@ -384,7 +383,6 @@ stocks.ba.bullbear,stocks_ba_stocktwits_model.get_bullbear,
 stocks.ba.cnews,stocks_ba_finnhub_model.get_company_news,
 stocks.ba.getdd,stocks_ba_reddit_model.get_due_dilligence,
 stocks.ba.headlines,stocks_ba_finbrain_model.get_sentiment,stocks_ba_finbrain_view.display_sentiment_analysis
-stocks.ba.infer,stocks_ba_twitter_model.load_analyze_tweets,stocks_ba_twitter_view.display_inference
 stocks.ba.mentions,stocks_ba_google_model.get_mentions,stocks_ba_google_view.display_mentions
 stocks.ba.messages,stocks_ba_stocktwits_model.get_messages,
 stocks.ba.ns,stocks_ba_news_sentiment_model.get_data,stocks_ba_news_sentiment_view.display_articles_data
@@ -393,7 +391,6 @@ stocks.ba.queries,stocks_ba_google_model.get_queries,
 stocks.ba.redditsent,stocks_ba_reddit_model.get_posts_about,
 stocks.ba.regions,stocks_ba_google_model.get_regions,stocks_ba_google_view.display_regions
 stocks.ba.rise,stocks_ba_google_model.get_rise,
-stocks.ba.sentiment,stocks_ba_twitter_model.get_sentiment,stocks_ba_twitter_view.display_sentiment
 stocks.ba.snews,stocks_ba_finnhub_model.get_headlines_sentiment,stocks_ba_finnhub_view.display_stock_price_headlines_sentiment
 stocks.ba.stalker,stocks_ba_stocktwits_model.get_stalker,
 stocks.ba.text_sent,stocks_ba_reddit_model.get_sentiment,
diff --git a/openbb_terminal/economy/datasets/cpi.csv b/openbb_terminal/economy/datasets/cpi.csv
index a209ce5869e..7671215ca5a 100644
--- a/openbb_terminal/economy/datasets/cpi.csv
+++ b/openbb_terminal/economy/datasets/cpi.csv
@@ -1,256 +1,256 @@
-title,country,frequency,units,series_id

-Consumer Price Index for Australia,australia,annual,growth_previous,CPALTT01AUA657N

-Consumer Price Index for Australia,australia,annual,growth_same,CPALTT01AUA659N

-Consumer Price Index for Australia,australia,quarterly,growth_previous,CPALTT01AUQ657N

-Consumer Price Index for Australia,australia,quarterly,growth_same,CPALTT01AUQ659N

-Consumer Price Index for Austria,austria,annual,growth_previous,CPALTT01ATA657N

-Consumer Price Index for Austria,austria,annual,growth_same,CPALTT01ATA659N

-Consumer Price Index for Austria,austria,monthly,growth_previous,CPALTT01ATM657N

-Consumer Price Index for Austria,austria,monthly,growth_same,CPALTT01ATM659N

-Consumer Price Index for Austria,austria,quarterly,growth_previous,CPALTT01ATQ657N

-Consumer Price Index for Austria,austria,quarterly,growth_same,CPALTT01ATQ659N

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-Consumer Price Index for Belgium,belgium,annual,growth_same,CPALTT01BEA659N

-Consumer Price Index for Belgium,belgium,monthly,growth_previous,CPALTT01BEM657N

-Consumer Price Index for Belgium,belgium,monthly,growth_same,CPALTT01BEM659N

-Consumer Price Index for Belgium,belgium,quarterly,growth_previous,CPALTT01BEQ657N

-Consumer Price Index for Belgium,belgium,quarterly,growth_same,CPALTT01BEQ659N

-Consumer Price Index for Brazil,brazil,annual,growth_previous,CPALTT01BRA657N

-Consumer Price Index for Brazil,brazil,annual,growth_same,CPALTT01BRA659N

-Consumer Price Index for Brazil,brazil,monthly,growth_previous,CPALTT01BRM657N

-Consumer Price Index for Brazil,brazil,monthly,growth_same,CPALTT01BRM659N

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