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+---
+layout: post
+title:  "Making a Line Plot"
+date:   2018-08-15 15:30:00 -0500
+author: "Kimberly Orr", "Nabarun Pal"
+categories: user-guide
+tags: "intro about line"
+excerpt_separator: <!--read more-->
+---
+
+# Making a Line Plot
+Altair works best with [long-form](https://altair-viz.github.io/user_guide/data.html#long-form-vs-wide-form-data) data. This is where each row contains a single observation along with all of its metadata stored as values.
+
+Matplotlib works a little better with wide-form data.
+
+Since mpl-altair converts from Altair to Matplotlib, let's look at how to create a line plot using Altair, Matplotlib, and mpl-altair with the following long-form data.
+
+```python
+import pandas as pd
+df = pd.DataFrame({
+    'set': [1, 2, 1, 2, 1, 2, 1, 2],
+    'amount': [1, 4, 5, 3, 1, 7, 2, 9],
+    'location': ['a', 'a', 'b', 'b', 'c', 'c', 'd', 'd']
+})
+```
+
+**set** | **amount** | **location**
+:---: | :---: | :---:
+1 | 1 | a
+2 | 4 | a
+1 | 5 | b
+2 | 3 | b
+1 | 1 | c
+2 | 7 | c
+1 | 2 | d
+2 | 9 | d
+
+A possible scenario for this dataset would be an experiment being run in several different locations with 2 measurements taken at each location. The goal of the visualization being to visualize how the amount changed between the two sets of measurements at each location.
+
+## Altair
+If we want to plot lines to show how each location changed between set one and set two,
+we need to
+1. specify the data: `alt.chart(df)`
+2. tell Altair to plot lines with `.mark_line()`
+3. link the encoding channels:
+    - x with 'set'
+    - y with 'amount'
+    - color with 'location'.
+```python
+import altair as alt
+alt.Chart(df).mark_line().encode(
+    alt.X('set'),
+    alt.Y('amount'),
+    alt.Color('location')
+)
+```
+![png](pics/altair-to-mpl-line_0.png)
+
+## Matplotlib
+In Matplotlib, just like with a [categorical scatter plot]({{ site.baseurl }}{% link _posts/2018-08-15-altair-to-mpl-scatter-part2.md %}), we have to plot a new line for every location.
+Specifying a label for each line allows us to generate a legend with `ax.legend()`.
+```python
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots()
+for loc, subset in df.groupby('location'):
+    ax.plot('set', 'amount', data=subset, label=loc)
+ax.set_xlabel('set')
+ax.set_ylabel('amount')
+ax.legend(title='location')
+plt.grid()
+plt.show()
+```
+![png](pics/altair-to-mpl-line_1.png)
+
+## mpl-altair
+To render the Altair chart using Matplotlib:
+```python
+import altair as alt
+import matplotlib.pyplot as plt
+import mplaltair
+chart = alt.Chart(df).mark_line().encode(
+    alt.X('set'),
+    alt.Y('amount'),
+    alt.Color('location')
+)
+fig, ax = mplaltair.convert(chart)
+plt.show()
+```
+![png](pics/altair-to-mpl-line_2.png)
+
+Since the Altair chart has been converted to Matplotlib objects, it's now possible to modify the chart using Matplotlib:
+```python
+import altair as alt
+import matplotlib.pyplot as plt
+import mplaltair
+chart = alt.Chart(df).mark_line().encode(
+    alt.X('set'),
+    alt.Y('amount'),
+    alt.Color('location')
+)
+fig, ax = mplaltair.convert(chart)
+ax.set_xlabel('set')
+ax.set_ylabel('amount')
+plt.show()
+```
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