diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md
new file mode 100644
index 0000000..53e6991
--- /dev/null
+++ b/.ipynb_checkpoints/README-checkpoint.md
@@ -0,0 +1,188 @@
+![logo_ironhack_blue 7](https://user-images.githubusercontent.com/23629340/40541063-a07a0a8a-601a-11e8-91b5-2f13e4e6b441.png)
+
+# MongoDB | Compass CRUD
+
+
+## 1. Installation
+
+### 1.1. Installation Mongo
+
+Link: https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/
+
+1. Execute in terminal one by one the following commands. 
+```
+sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 9DA31620334BD75D9DCB49F368818C72E52529D4
+echo "deb [ arch=amd64 ] https://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.0.list
+sudo apt-get update
+sudo apt-get install -y mongodb-org
+echo "mongodb-org hold" | sudo dpkg --set-selections
+echo "mongodb-org-server hold" | sudo dpkg --set-selections
+echo "mongodb-org-shell hold" | sudo dpkg --set-selections
+echo "mongodb-org-mongos hold" | sudo dpkg --set-selections
+echo "mongodb-org-tools hold" | sudo dpkg --set-selections
+```
+
+2. Check if works.
+```
+sudo service mongod start
+```
+3. Check the log, we should see something like **[initandlisten] waiting for connections on port 27017**
+```
+sudo cat /var/log/mongodb/mongod.log
+```
+4. Reset service.
+```
+sudo service mongod stop
+```
+5. Reboot service to keep it working.
+```
+sudo service mongod restart
+mongo
+```
+
+### 1.2. Installation Compass
+Link: https://docs.mongodb.com/compass/master/install/
+
+1. Download from link: https://www.mongodb.com/download-center/compass?jmp=hero
+2. Execute the downloaded file.
+3. Now you've got mongodb-compass.
+
+
+## 2. Import database to mongodb
+1. Start mongodb 
+```
+sudo service mongod restart
+```
+2. Open compass 
+3. Create database (green button) and select the Database fields name: db_companies, Collection name: companies. 
+![](images/01-create-database.png)
+![](images/02-create-database.png)
+![](images/03-compass-mongo-companies.png)
+
+4. Import json file to mongo: 
+```
+mongoimport --db db_companies --collection companies companies.json
+```
+
+## 3. pymongo and filtered out
+
+### 3.1 requirements
+```
+pip3 install pymongo
+```
+
+Now open jupyter notebook
+```
+jupyter notebook
+```
+
+### 3.2 Connection to the database
+
+```Python
+# imports
+from pymongo import MongoClient
+import pandas as pd
+
+# connection with mongo
+client = MongoClient("mongodb://localhost:27017/")
+
+# connection with database "db_companies"
+db = client.db_companies
+
+# companies collection
+collection_companies = db.companies
+
+# querying EVERYTHING
+query = collection_companies.find()
+
+# convert to pandas.DataFrame
+df = pd.DataFrame(query)
+```
+
+
+
+
+## Introduction Lab
+
+We are back with our queries! :wink:
+
+We have learned some super useful query operators, that will helps us to make much better queries to retrieve the data we need. We will continue using the **Crunchbase** database we used on the last exercise.
+
+
+
+## Submission
+
+- Upon completion, run the following commands
+```
+$ git add .
+$ git commit -m "done"
+$ git push origin master
+```
+- Create Pull Request so your TAs can check up your work.
+
+
+## Deliverables
+
+Since we will be querying our database from Mongo Compass, you will need to copy/paste the `query`, `projection`, `sort`, `skip` and `limit` you entered on Mongo Compass. In the `queries.md` file, you will find the instructions about the queries you need to do, and a field to fill the answers.
+
+### Example
+
+1. This is an example
+ - **`query`**: /*You should copy/paste the query in here*/
+ - **`projection`**: /*You should copy/paste the projection in here*/
+ - **`sort`**: /*You should copy/paste the sort in here*/
+ - **`skip`**: /*You should copy/paste the skip in here*/
+ - **`limit`**: /*You should copy/paste the limit in here*/
+
+## Instructions
+
+### Iteration 1
+
+First, we need to import the database we will be using for the `lab`. We will use the Crunchbase database. Crunchbase is the premier destination for discovering industry trends, investments, and news about hundreds of thousands of companies globally. From startups to Fortune 500s, Crunchbase is recognized as the primary source of company intelligence by millions of users globally.
+
+The database contains more than 18k documents, and each of them has a lot of information about each of the companies. A document looks like the following:
+
+![image](https://user-images.githubusercontent.com/23629340/36494916-d6db1770-1733-11e8-903e-5119b3c1b688.png)
+
+1. You will find the `.zip` file of the Database on the **lab** folder.
+2. Unzip the file
+3. From the terminal, import the database to Mongo using the following command:
+```bash
+$ mongoimport --db companies --collection companies --file companies.json
+```
+4. Check on Mongo Compass if everything goes ok:
+
+:::info >
+When running the `mongoimport` you should be located in the same folder as the `companies.json` file.
+:::
+
+![image](https://user-images.githubusercontent.com/23629340/36534191-1f1bc5ec-17c6-11e8-9463-4945679b98c0.png)
+
+
+### Iteration 2
+
+You already know how this goes, so let's start working:
+
+1. All the companies that it's name match 'Babelgum'. Retrieve only their `name` field.
+2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**.
+3. All the companies founded between 2000 and 2005, both years included. Retrieve only the `name` and `founded_year` fileds.
+4. All the companies that had a Valuation Amount of more than 100.000.000 and have been founded before 2010. Retrieve only the `name` and `ipo` fields.
+5. All the companies that have less than 1000 employees and have been founded before 2005. Order them by the number of employees and limit the search to 10 companies.
+6. All the companies that don't include the `partners` field.
+7. All the companies that have a null type of value on the `category_code` field.
+8. All the companies that have at least 100 employees but less than 1000. Retrieve only the `name` and `number of employees` fields.
+9. Order all the companies by their IPO price descendently.
+10. Retrieve the 10 companies with more employees, order by the `number of employees`
+11. All the companies founded on the second semester of the year. Limit your search to 1000 companies.
+12. All the companies that have been 'deadpooled' after the third year.
+13. All the companies founded before 2000 that have and acquisition amount of more than 10.000.000
+14. All the companies that have been acquired after 2015, order by the acquisition amount, and retrieve only their `name` and `acquisiton` field.
+15. Order the companies by their `founded year`, retrieving only their `name` and `founded year`.
+16. All the companies that have been founded on the first seven days of the month, including the seventh. Sort them by their `aquisition price` descendently. Limit the search to 10 documents.
+17. All the companies on the 'web' `category` that have more than 4000 employees. Sort them by the amount of employees in ascendant order.
+18. All the companies which their acquisition amount is more than 10.000.000, and currency are 'EUR'.
+19. All the companies that have been acquired on the first trimester of the year. Limit the search to 10 companies, and retrieve only their `name` and `acquisition` fields.
+20. All the companies that have been founded between 2000 and 2010, but have not been acquired before 2011.
+
+
+Happy Coding! :heart:
diff --git a/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb b/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb
new file mode 100644
index 0000000..6eee69f
--- /dev/null
+++ b/starter_code/.ipynb_checkpoints/queries-checkpoint.ipynb
@@ -0,0 +1,330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Ironhack Logo](https://i.imgur.com/1QgrNNw.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymongo import MongoClient\n",
+    "client=MongoClient()\n",
+    "db=client.companies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Answers\n",
+    "### 1. All the companies that it's name match 'Babelgum'. Retrieve only their `name` field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. All the companies founded between 2000 and 2005, both years included. Retrieve only the `name` and `founded_year` fileds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. All the companies that had a Valuation Amount of more than 100.000.000 and have been founded before 2010. Retrieve only the `name` and `ipo` fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "                                   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. All the companies that have less than 1000 employees and have been founded before 2005. Order them by the number of employees and limit the search to 10 companies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. All the companies that don't include the `partners` field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. All the companies that have a null type of value on the `category_code` field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8. All the companies that have at least 100 employees but less than 1000. Retrieve only the `name` and `number of employees` fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9. Order all the companies by their IPO price descendently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10. Retrieve the 10 companies with more employees, order by the `number of employees`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 11. All the companies founded on the second semester of the year. Limit your search to 1000 companies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 12. All the companies that have been 'deadpooled' after the third year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 13. All the companies founded before 2000 that have and acquisition amount of more than 10.000.000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 14. All the companies that have been acquired after 2015, order by the acquisition amount, and retrieve only their `name` and `acquisiton` field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 15. Order the companies by their `founded year`, retrieving only their `name` and `founded year`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 16. All the companies that have been founded on the first seven days of the month, including the seventh. Sort them by their `aquisition price` descendently. Limit the search to 10 documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 17. All the companies on the 'web' `category` that have more than 4000 employees. Sort them by the amount of employees in ascendant order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 18. All the companies which their acquisition amount is more than 10.000.000, and currency are 'EUR'.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 19. All the companies that have been acquired on the first trimester of the year. Limit the search to 10 companies, and retrieve only their `name` and `acquisition` fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 20. All the companies that have been founded between 2000 and 2010, but have not been acquired before 2011."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/starter_code/.ipynb_checkpoints/queries-checkpoint.md b/starter_code/.ipynb_checkpoints/queries-checkpoint.md
new file mode 100644
index 0000000..cae5b19
--- /dev/null
+++ b/starter_code/.ipynb_checkpoints/queries-checkpoint.md
@@ -0,0 +1,83 @@
+![Ironhack Logo](https://i.imgur.com/1QgrNNw.png)
+
+# Answers
+
+### 1. All the companies that it's name match 'Babelgum'. Retrieve only their `name` field.
+
+query: {name : "Babelgum"}
+
+### 2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**.
+
+query: {number_of_employees: {$gt : 500}} sort: {number_of_employees : 1} limit: 20
+
+### 3. All the companies founded between 2000 and 2005, both years included. Retrieve only the `name` and `founded_year` fileds.
+
+query: {founded_year: {$gte : 2000, $lt : 2005}} projection: {name:1, founded_year:1}
+
+### 4. All the companies that had a Valuation Amount of more than 100.000.000 and have been founded before 2010. Retrieve only the `name` and `ipo` fields.
+
+query: {"ipo.valuation_amount": {$gt : 100000000}, founded_year:{$lt:2010}} projection: {ipo:1, name:1}
+
+### 5. All the companies that have less than 1000 employees and have been founded before 2005. Order them by the number of employees and limit the search to 10 companies.
+
+<!-- Your Code Goes Here -->
+
+### 6. All the companies that don't include the `partners` field.
+
+<!-- Your Code Goes Here -->
+
+### 7. All the companies that have a null type of value on the `category_code` field.
+
+query: {category_code : null}
+
+### 8. All the companies that have at least 100 employees but less than 1000. Retrieve only the `name` and `number of employees` fields.
+
+query: {number_of_employees:{$gte : 100, $lt:1000}} projection: {name : 1, number_of_employees:1}
+
+### 9. Order all the companies by their IPO price descendently.
+
+sort: {"ipo.valuation_amount": -1}
+
+### 10. Retrieve the 10 companies with more employees, order by the `number of employees`
+
+ort: {number_of_employees:-1} limit: 10
+
+### 11. All the companies founded on the second semester of the year. Limit your search to 1000 companies.
+
+query: {founded_month:{$gte:7, $lte:12}} limit: 1000
+
+### 12. All the companies that have been 'deadpooled' after the third year.
+
+<!-- Your Code Goes Here -->
+
+### 13. All the companies founded before 2000 that have and acquisition amount of more than 10.000.000
+
+<!-- Your Code Goes Here -->
+
+### 14. All the companies that have been acquired after 2015, order by the acquisition amount, and retrieve only their `name` and `acquisiton` field.
+
+query: {"acquisition.acquired_year":{$gt:2015}} projection: {name:1, acquisition:1} sort: {"acquisition.price_amount":1}
+
+### 15. Order the companies by their `founded year`, retrieving only their `name` and `founded year`.
+
+projection: {name:1, founded_year:1, _id:0} sort: {founded_year:1}
+
+### 16. All the companies that have been founded on the first seven days of the month, including the seventh. Sort them by their `aquisition price` descendently. Limit the search to 10 documents.
+
+<!-- Your Code Goes Here -->
+
+### 17. All the companies on the 'web' `category` that have more than 4000 employees. Sort them by the amount of employees in ascendant order.
+
+query: {category_code:"web", number_of_employees:{$gt:4000}} sort: {number_of_employees:1}
+
+### 18. All the companies which their acquisition amount is more than 10.000.000, and currency are 'EUR'.
+
+<!-- Your Code Goes Here -->
+
+### 19. All the companies that have been acquired on the first trimester of the year. Limit the search to 10 companies, and retrieve only their `name` and `acquisition` fields.
+
+query: {"acquisition.acquired_month":{$lte:3}} projection: {name:1, acquisition:1, _id:0} limit: 10
+
+### 20. All the companies that have been founded between 2000 and 2010, but have not been acquired before 2011.
+
+<!-- Your Code Goes Here -->
diff --git a/starter_code/queries.md b/starter_code/queries.md
index cb54f8a..cae5b19 100644
--- a/starter_code/queries.md
+++ b/starter_code/queries.md
@@ -4,19 +4,19 @@
 
 ### 1. All the companies that it's name match 'Babelgum'. Retrieve only their `name` field.
 
-<!-- Your Code Goes Here -->
+query: {name : "Babelgum"}
 
 ### 2. All the companies that have more than 5000 employees. Limit the search to 20 companies and sort them by **number of employees**.
 
-<!-- Your Code Goes Here -->
+query: {number_of_employees: {$gt : 500}} sort: {number_of_employees : 1} limit: 20
 
 ### 3. All the companies founded between 2000 and 2005, both years included. Retrieve only the `name` and `founded_year` fileds.
 
-<!-- Your Code Goes Here -->
+query: {founded_year: {$gte : 2000, $lt : 2005}} projection: {name:1, founded_year:1}
 
 ### 4. All the companies that had a Valuation Amount of more than 100.000.000 and have been founded before 2010. Retrieve only the `name` and `ipo` fields.
 
-<!-- Your Code Goes Here -->
+query: {"ipo.valuation_amount": {$gt : 100000000}, founded_year:{$lt:2010}} projection: {ipo:1, name:1}
 
 ### 5. All the companies that have less than 1000 employees and have been founded before 2005. Order them by the number of employees and limit the search to 10 companies.
 
@@ -28,23 +28,23 @@
 
 ### 7. All the companies that have a null type of value on the `category_code` field.
 
-<!-- Your Code Goes Here -->
+query: {category_code : null}
 
 ### 8. All the companies that have at least 100 employees but less than 1000. Retrieve only the `name` and `number of employees` fields.
 
-<!-- Your Code Goes Here -->
+query: {number_of_employees:{$gte : 100, $lt:1000}} projection: {name : 1, number_of_employees:1}
 
 ### 9. Order all the companies by their IPO price descendently.
 
-<!-- Your Code Goes Here -->
+sort: {"ipo.valuation_amount": -1}
 
 ### 10. Retrieve the 10 companies with more employees, order by the `number of employees`
 
-<!-- Your Code Goes Here -->
+ort: {number_of_employees:-1} limit: 10
 
 ### 11. All the companies founded on the second semester of the year. Limit your search to 1000 companies.
 
-<!-- Your Code Goes Here -->
+query: {founded_month:{$gte:7, $lte:12}} limit: 1000
 
 ### 12. All the companies that have been 'deadpooled' after the third year.
 
@@ -56,11 +56,11 @@
 
 ### 14. All the companies that have been acquired after 2015, order by the acquisition amount, and retrieve only their `name` and `acquisiton` field.
 
-<!-- Your Code Goes Here -->
+query: {"acquisition.acquired_year":{$gt:2015}} projection: {name:1, acquisition:1} sort: {"acquisition.price_amount":1}
 
 ### 15. Order the companies by their `founded year`, retrieving only their `name` and `founded year`.
 
-<!-- Your Code Goes Here -->
+projection: {name:1, founded_year:1, _id:0} sort: {founded_year:1}
 
 ### 16. All the companies that have been founded on the first seven days of the month, including the seventh. Sort them by their `aquisition price` descendently. Limit the search to 10 documents.
 
@@ -68,7 +68,7 @@
 
 ### 17. All the companies on the 'web' `category` that have more than 4000 employees. Sort them by the amount of employees in ascendant order.
 
-<!-- Your Code Goes Here -->
+query: {category_code:"web", number_of_employees:{$gt:4000}} sort: {number_of_employees:1}
 
 ### 18. All the companies which their acquisition amount is more than 10.000.000, and currency are 'EUR'.
 
@@ -76,7 +76,7 @@
 
 ### 19. All the companies that have been acquired on the first trimester of the year. Limit the search to 10 companies, and retrieve only their `name` and `acquisition` fields.
 
-<!-- Your Code Goes Here -->
+query: {"acquisition.acquired_month":{$lte:3}} projection: {name:1, acquisition:1, _id:0} limit: 10
 
 ### 20. All the companies that have been founded between 2000 and 2010, but have not been acquired before 2011.