Dina

log.md

@@ -1,37 +1,71 @@
 # Master Python for Data Science Handbook - Log
 
 ## Week 1: Introduction: IPython: Beyond Normal Python
-**(delete me or comment me out)**
 
-**Today's Progress**: Finished going through Numpy and Pandas
+**This Week's Progress**: Finished going through the basics of IPython.
+In particular, I learned a lot of new commands on IPython. These include:
+* Accessing Documentation with ``?``
+* Accessing Source Code with ``??``
+* Magic commands
+* Profiling and timing code
 
-**Thoughts**: I really struggled through..., but I feel like I am slowly getting better at it
+**Thoughts**: I really struggled through ``Errors and Debugging``, but I feel like I am slowly getting better at it. The magic commands ``%paste`` and ``%cpaste`` didn't work.
 
-**Link to work**: [introduction(Ipython)](www.example.com)
+**Link to work**: [Chapter 1](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter1.ipynb)
 
 ## Week 2: Chapter 2: Introduction to Numpy
 
 **This week's Progress**:
+1. Understanding Data Types in Python
 
-**Thoughts**:
+2. The Basics of Numpy Arrays
 
-**Link to work**:
+3. Computation on Numpy Arrays
 
-## Week 3: Chapter 3: Data Manipulation with Pandas
+4. Aggregations
 
-**This week's Progress**:
+5. Computations on Arrays
 
-**Thoughts**:
+6. Comparisons, Masks, and Boolean Logic
 
-**Link to work**:
+7. Fancy Indexing
+
+8. Sorting Arrays
+
+9. Structured Data
+
+**Thoughts**: I  have learned new NumPy operations. The computation and aggregation techniques, broadcasting techniques are very crucial. Fancy indexing was challenging.
+
+**Link to work**:[Chapter 2](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter2.ipynb)
+
+## Week 3: Chapter 3: Data Manipulation with Pandas
+
+**This week's Progress**:  
+The focus on Pandas:
+* [Introducing Pandas Objects](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_01_Introducing_Pandas_Objects.ipynb)
+* [Data Indexing and Selection](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_02_Data_Indexing_and_Selection.ipynb)
+* [Operating on Data in Pandas](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_03_Operations_in_Pandas.ipynb)
+* [Handling Missing Data](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_04_Missing_Values.ipynb)
+* [Hierarchical Indexing](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_05_Hierarchical_Indexing.ipynb)
+* [Combining Datasets: Concat and Append](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_06_Concat_And_Append.ipynb)
+* [Combining Datasets: Merge and Join](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_07_Merge_and_Join.ipynb)
+* [Aggregation and Grouping](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_08_Aggregation_and_Grouping.ipynb)
+* [Pivot Tables](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_09_Pivot_Tables.ipynb)
+* [Vectorized String Operations](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_10_Working_With_Strings.ipynb)
+* [Working with Time Series](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_11_Working_with_Time_Series.ipynb)
+* [High-Performance Pandas: eval() and query()](https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Chapter3_12_Performance_Eval_and_Query.ipynb)
+
+**Thoughts**: Data Manipulation with Pandas builds on NumPy with a lot more techniques.
+
+**Link to work**: Indicated on week 3 progress above. The notebooks are customized from the [textbook](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master/notebooks)
 
 ## Week 4: Practice(Challenge 1)
 
-**This week's Progress**:
+**This week's Progress**: Completed the analytics on Kiva data for loans across countries in terms of gender and sector.
 
-**Thoughts**:
+**Thoughts**: All the concepts of Pandas are applied to the challenge.
 
-**Link to work**:
+**Link to work**: [Kiva Challenge Submission]( https://github.com/twiga2/Python4ds_cohort-1/blob/dina/work/Challenge1_Kiva.ipynb)
 
 ## Week 5: Chapter 4: Data Visualization
 
@@ -88,4 +122,3 @@
 * [Log - click here to see my progress](https://github.com/Python-4-DS/Python4ds_cohort-1/blob/master/log.md)
 * [Resources](https://github.com/Python-4-DS/Python4ds_cohort-1/blob/master/resources.md)
 * [FAQ](https://github.com/Python-4-DS/Python4ds_cohort-1/blob/master/FAQ.md)
-

work/.ipynb_checkpoints/03_13_Further_Resources-checkpoint.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "zpYwA1SHgZh8"
+   },
+   "source": [
+    "<!--BOOK_INFORMATION-->\n",
+    "<img align=\"left\" style=\"padding-right:10px;\" src=\"https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/figures/PDSH-cover-small.png?raw=1\">\n",
+    "\n",
+    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+    "\n",
+    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "HhrfQKlCgZh9"
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Qi1YhZ5agZh-"
+   },
+   "source": [
+    "# Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3wksIzkEgZiA"
+   },
+   "source": [
+    "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis.\n",
+    "Still, much has been omitted from our discussion.\n",
+    "To learn more about Pandas, I recommend the following resources:\n",
+    "\n",
+    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
+    "\n",
+    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n",
+    "\n",
+    "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.\n",
+    "\n",
+    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
+    "\n",
+    "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "7Li3j4iCgZiB"
+   },
+   "source": [
+    "<!--NAVIGATION-->\n",
+    "< [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb) | [Contents](Index.ipynb) | [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) >\n",
+    "\n",
+    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "03.13-Further-Resources.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}