Develop Reference

Groupby data in Pandad to label my pie chart

I have using groupby in Pandas,
df_0 = df_find_quality_und.groupby("user score").size()
df_0
and get this output;
Output:
user score
3.0 14
7.0 2
7.1 1
7.2 2
7.3 1
7.5 1
7.7 1
7.8 2
7.9 1
8.0 1
8.1 2
8.3 1
8.7 1
dtype: int64
The right side is the frequency of the the data.
Then, I want to plot this data into a pie-chart. But I don't know how to take the left data only to become the labels of my pie-chart. Hence, I write it manually like this;
user_sc_und = [3.0,7.0,7.1,7.2,7.3,7.5,7.7,7.8,7.9,8.0,8.1,8.3,8.7]
user_sc_und_1 = df_find_quality_und.groupby("user score")
plot = plt.pie(df_0, labels=user_sc_und)
The pie-chart was well shown in the output with the label. But, I don't want to write the labels manually every time.. Can anyone help me on how to label the pie-chart by taking the groupby data on the left side?
Thank you.

Pandas allows you to plot pie chart:
df_0 = df_find_quality_und.groupby("user score").size()
df_0.plot.pie(autopct="%.2f",figsize=(10,10))
output:

The labels are actually the index of the groupby sizes object, so, you can try:
plot = plt.pie(df_0, labels=df_0.index)

Plotting multiple groups from a dataframe with datashader as lines

I am trying to make plots with datashader. the data itself is a time series of points in polar coordiantes. i managed to transform them to cartesian coordianted(to have equal spaced pixles) and i can plot them with datashader.
the point where i am stuck is that if i just plot them with line() instead of points() it just connects the whole dataframe as a single line. i would like to plot the data of the dataframe group per group(the groups are the names in list_of_names ) onto the canvas as lines.
data can be found here
i get this kind of image with datashader
This is a zoomed in view of the plot generated with points() instead of line() the goal is to produce the same plot but with connected lines instead of points
import datashader as ds, pandas as pd, colorcet
import numby as np
df = pd.read_csv('file.csv')
print(df)
starlink_name = df.loc[:,'Name']
starlink_alt = df.loc[:,'starlink_alt']
starlink_az = df.loc[:,'starlink_az']
name = starlink_name.values
alt = starlink_alt.values
az = starlink_az.values
print(name)
print(df['Name'].nunique())
df['Date'] = pd.to_datetime(df['Date'])
for name, df_name in df.groupby('Name'):
print(name)
df_grouped = df.groupby('Name')
list_of_names = list(df_grouped.groups)
print(len(list_of_names))
#########################################################################################
#i want this kind of plot with connected lines with datashader
#########################################################################################
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8], polar=True)
# ax.invert_yaxis()
ax.set_theta_zero_location('N')
ax.set_rlim(90, 60, 1)
# Note: you must set the end of arange to be slightly larger than 90 or it won't include 90
ax.set_yticks(np.arange(0, 91, 15))
ax.set_rlim(bottom=90, top=0)
for name in list_of_names:
df2 = df_grouped.get_group(name)
ax.plot(np.deg2rad(df2['starlink_az']), df2['starlink_alt'], linestyle='solid', marker='.',linewidth=0.5, markersize=0.1)
plt.show()
print(df)
#########################################################################################
#transformation to cartasian coordiantes
#########################################################################################
df['starlink_alt'] = 90 - df['starlink_alt']
df['x'] = df.apply(lambda row: np.deg2rad(row.starlink_alt) * np.cos(np.deg2rad(row.starlink_az)), axis=1)
df['y'] = df.apply(lambda row: -1 * np.deg2rad(row.starlink_alt) * np.sin(np.deg2rad(row.starlink_az)), axis=1)
#########################################################################################
# this is what i want but as lines group per group
#########################################################################################
cvs = ds.Canvas(plot_width=2000, plot_height=2000)
agg = cvs.points(df, 'y', 'x')
img = ds.tf.shade(agg, cmap=colorcet.fire, how='eq_hist')
#########################################################################################
#here i am stuck
#########################################################################################
for name in list_of_names:
df2 = df_grouped.get_group(name)
cvs = ds.Canvas(plot_width=2000, plot_height=2000)
agg = cvs.line(df2, 'y', 'x')
img = ds.tf.shade(agg, cmap=colorcet.fire, how='eq_hist')
#plt.imshow(img)
plt.show()

To do this, you have a couple options. One is inserting NaN rows as a breakpoint into your dataframe when using cvs.line. You need DataShader to "pick up the pen" as it were, by inserting a row of NaNs after each group. It's not the slickest, but that's a current recommended solution.
Really simple, hacky example:
In [17]: df = pd.DataFrame({
...: 'name': list('AABBCCDD'),
...: 'x': np.arange(8),
...: 'y': np.arange(10, 18),
...: })
In [18]: df
Out[18]:
name x y
0 A 0 10
1 A 1 11
2 B 2 12
3 B 3 13
4 C 4 14
5 C 5 15
6 D 6 16
7 D 7 17
This block groups on the 'name' column, then reindexes each group to be one element longer than the original data:
In [20]: res = df.set_index('name').groupby('name').apply(
...: lambda x: x.reset_index(drop=True).reindex(np.arange(len(x) + 1))
...: )
In [21]: res
Out[21]:
x y
name
A 0 0.0 10.0
1 1.0 11.0
2 NaN NaN
B 0 2.0 12.0
1 3.0 13.0
2 NaN NaN
C 0 4.0 14.0
1 5.0 15.0
2 NaN NaN
D 0 6.0 16.0
1 7.0 17.0
2 NaN NaN
You can plug this reindexed dataframe into datashader to have multiple disconnected lines in the result.
This is a still-open issue on the datashader repo, including additional examples and boilerplate code: https://github.com/holoviz/datashader/issues/257
Other options include restructuring your data to accommodate one of cvs.line's other formats. From the Canvas.line docstring:
def line(self, source, x=None, y=None, agg=None, axis=0, geometry=None,
antialias=False):
Parameters
----------
source : pandas.DataFrame, dask.DataFrame, or xarray.DataArray/Dataset
The input datasource.
x, y : str or number or list or tuple or np.ndarray
Specification of the x and y coordinates of each vertex
* str or number: Column labels in source
* list or tuple: List or tuple of column labels in source
* np.ndarray: When axis=1, a literal array of the
coordinates to be used for every row
agg : Reduction, optional
Reduction to compute. Default is ``any()``.
axis : 0 or 1, default 0
Axis in source to draw lines along
* 0: Draw lines using data from the specified columns across
all rows in source
* 1: Draw one line per row in source using data from the
specified columns
There are a number of additional examples in the cvs.line docstring. You can pass arrays as the x, y arguments giving multiple columns to use in forming lines when axis=1, or you can a dataframe with ragged array values.
See this pull request adding the line options (h/t to #James-a-bednar in the comments) for a discussion of their use.

Matplotlib DataFrame boxplot with given max,min and quaritles

I want to plot a box plot with my DataFrame:
A B C
max 10 11 14
min 3 4 10
q1 5 6 12
q3 9 7 13
how can I plot a box plot with these fixed values?

You can use the Axes.bxp method in matplotlib, based on this helpful answer. The input is a list of dictionaries containing the relevant values, but the median is a required key in these dictionaries. Since the data you provided does not include medians, I have made up medians in the code below (but you will need to calculate them from your actual data).
import matplotlib.pyplot as plt
import pandas as pd
# reproducing your data
df = pd.DataFrame({'A':[10,3,5,9],'B':[11,4,6,7],'C':[14,10,12,13]})
# add a row for median, you need median values!
sample_medians = {'A':7, 'B':6.5, 'C':12.5}
df = df.append(sample_medians, ignore_index=True)
df.index = ['max','min','q1','q3','med']
Here is the modified df with medians included:
>>> df
A B C
max 10.0 11.0 14.0
min 3.0 4.0 10.0
q1 5.0 6.0 12.0
q3 9.0 7.0 13.0
med 7.0 6.5 12.5
Now we transform the df into a list of dictionaries:
labels = list(df.columns)
# create dictionaries for each column as items of a list
bxp_stats = df.apply(lambda x: {'med':x.med, 'q1':x.q1, 'q3':x.q3, 'whislo':x['min'], 'whishi':x['max']}, axis=0).tolist()
# add the column names as labels to each dictionary entry
for index, item in enumerate(bxp_stats):
item.update({'label':labels[index]})
_, ax = plt.subplots()
ax.bxp(bxp_stats, showfliers=False);
plt.show()
Unfortunately the median line is a required parameter so it must be specified for every box. Therefore we just make it as thin as possible to be virtually unseeable.
If you want each box to be drawn with different specifications, they will have to be in different subplots. I understand if this looks kind of ugly, so you can play around with the spacing between subplots or consider removing some of the y-axes.
fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True)
# specify list of background colors, median line colors same as background with as thin of a width as possible
colors = ['LightCoral', '#FEF1B5', '#EEAEEE']
medianprops = [dict(linewidth = 0.1, color='LightCoral'), dict(linewidth = 0.1, color='#FEF1B5'), dict(linewidth = 0.1, color='#EEAEEE')]
# create a list of boxplots of length 3
bplots = [axes[i].bxp([bxp_stats[i]], medianprops=medianprops[i], patch_artist=True, showfliers=False) for i in range(len(df.columns))]
# set each boxplot a different color
for i, bplot in enumerate(bplots):
for patch in bplot['boxes']:
patch.set_facecolor(colors[i])
plt.show()

Is there a way where I can plot data from a CSV file where every 10 data points in a column are a different line in the same graph?

Say I have a data set that is in two columns. I want to plot a line plot iterating through every 10. So, I would take the first 10, and then the second 10, which is right under the first 10, for another line plot on the same graph (different color line). The data is stacked on each other in a CSV file with no header.
Currently, I have it taking in the entire column. It plots them, however there is no differentiation as to which data set it is. I want to plot multiple lines on the same graph but the CSV file has all the data sets in one column, but I need to graph every 10.
EDIT
Below I have Data added I would like the first column to be the x-axis and the second to be the y.
Sample Data:
0 8.2
1 9.1
2 2.2
3 3.3
4 9.8
5 6.3
6 4.8
7 8.6
8 3.9
9 2.1
0 9.34
1 10.2
2 7.22
3 6.98
4 1.34
5 2.56
6 6.78
7 4.56
8 3.3
9 9.4

OK, try this:
# this is the toy data
df = pd.DataFrame({0:list(range(10))*2,
1:np.random.uniform(9,11,20)})
# set up axes for plots
fig, ax = plt.subplots(1,1)
# the groupby argument groups every 10 rows together
# then pass it to the `lambda` function,
# which plots each chunk to the given plt axis
df.groupby(df.reset_index().index//10).apply(lambda x: ax.plot(x[0], x[1]) )
plt.show()
Option 2:
I found sns is a better tool for the purpose:
fig, ax = plt.subplots(1,1, figsize=(10,6))
sns.lineplot(x=df[0],
y=df[1],
hue=df.reset_index().index//10,
data=df,
palette='Set1')
plt.show()
outputs:

Grouping boxplots in seaborn when input is a DataFrame

I intend to plot multiple columns in a pandas dataframe, all grouped by another column using groupby inside seaborn.boxplot. There is a nice answer here, for a similar problem in matplotlib matplotlib: Group boxplots but given the fact that seaborn.boxplot comes with groupby option I thought it could be much easier to do this in seaborn.
Here we go with a reproducible example that fails:
import seaborn as sns
import pandas as pd
df = pd.DataFrame([[2, 4, 5, 6, 1], [4, 5, 6, 7, 2], [5, 4, 5, 5, 1],
[10, 4, 7, 8, 2], [9, 3, 4, 6, 2], [3, 3, 4, 4, 1]],
columns=['a1', 'a2', 'a3', 'a4', 'b'])
# display(df)
a1 a2 a3 a4 b
0 2 4 5 6 1
1 4 5 6 7 2
2 5 4 5 5 1
3 10 4 7 8 2
4 9 3 4 6 2
5 3 3 4 4 1
#Plotting by seaborn
sns.boxplot(df[['a1','a2', 'a3', 'a4']], groupby=df.b)
What I get is something that completely ignores groupby option:
Whereas if I do this with one column it works thanks to another SO question Seaborn groupby pandas Series :
sns.boxplot(df.a1, groupby=df.b)
So I would like to get all my columns in one plot (all columns come in a similar scale).
EDIT:
The above SO question was edited and now includes a 'not clean' answer to this problem, but it would be nice if someone has a better idea for this problem.

As the other answers note, the boxplot function is limited to plotting a single "layer" of boxplots, and the groupby parameter only has an effect when the input is a Series and you have a second variable you want to use to bin the observations into each box..
However, you can accomplish what I think you're hoping for with the factorplot function, using kind="box". But, you'll first have to "melt" the sample dataframe into what is called long-form or "tidy" format where each column is a variable and each row is an observation:
df_long = pd.melt(df, "b", var_name="a", value_name="c")
Then it's very simple to plot:
sns.factorplot("a", hue="b", y="c", data=df_long, kind="box")

You can use directly boxplot (I imagine when the question was asked, that was not possible, but with seaborn version > 0.6 it is).
As explained by #mwaskom, you have to "melt" the sample dataframe into its "long-form" where each column is a variable and each row is an observation:
df_long = pd.melt(df, "b", var_name="a", value_name="c")
# display(df_long.head())
b a c
0 1 a1 2
1 2 a1 4
2 1 a1 5
3 2 a1 10
4 2 a1 9
Then you just plot it:
sns.boxplot(x="a", hue="b", y="c", data=df_long)

Seaborn's groupby function takes Series not DataFrames, that's why it's not working.
As a work around, you can do this :
fig, ax = plt.subplots(1,2, sharey=True)
for i, grp in enumerate(df.filter(regex="a").groupby(by=df.b)):
sns.boxplot(grp[1], ax=ax[i])
it gives :
Note that df.filter(regex="a") is equivalent to df[['a1','a2', 'a3', 'a4']]
a1 a2 a3 a4
0 2 4 5 6
1 4 5 6 7
2 5 4 5 5
3 10 4 7 8
4 9 3 4 6
5 3 3 4 4
Hope this helps

It isn't really any better than the answer you linked, but I think the way to achieve this in seaborn is using the FacetGrid feature, as the groupby parameter is only defined for Series passed to the boxplot function.
Here's some code - the pd.melt is necessary because (as best I can tell) the facet mapping can only take individual columns as parameters, so the data need to be turned into a 'long' format.
g = sns.FacetGrid(pd.melt(df, id_vars='b'), col='b')
g.map(sns.boxplot, 'value', 'variable')

It's not adding a lot to this conversation, but after struggling with this for longer than warranted (the actual clusters are unusable), I thought I would add my implementation as another example. It's got a superimposed scatterplot (because of how annoying my dataset is), shows melt using indices, and some aesthetic tweaks. I hope this is useful for someone.
output_graph
Here it is without using column headers (I saw a different thread that wanted to know how to do this using indices):
combined_array: ndarray = np.concatenate([dbscan_output.data, dbscan_output.labels.reshape(-1, 1)], axis=1)
cluster_data_df: DataFrame = DataFrame(combined_array)
if you want to use labelled columns:
column_names: List[str] = list(outcome_variable_names)
column_names.append('cluster')
cluster_data_df.set_axis(column_names, axis='columns', inplace=True)
graph_data: DataFrame = pd.melt(
frame=cluster_data_df,
id_vars=['cluster'],
# value_vars is an optional param - by default it uses columns except the id vars, but I've included it as an example
# value_vars=['outcome_var_1', 'outcome_var_2', 'outcome_var_3', 'outcome_var_4', 'outcome_var_5', 'outcome_var_6']
var_name='psychometric_test',
value_name='standard deviations from the mean'
)
The resulting dataframe (rows = sample_n x variable_n (in my case 1626 x 6 = 9756)):
index
cluster
psychometric_tst
standard deviations from the mean
0
0.0
outcome_var_1
-1.276182
1
0.0
outcome_var_1
-1.118813
2
0.0
outcome_var_1
-1.276182
9754
0.0
outcome_var_6
0.892548
9755
0.0
outcome_var_6
1.420480
If you want to use indices with melt:
graph_data: DataFrame = pd.melt(
frame=cluster_data_df,
id_vars=cluster_data_df.columns[-1],
# value_vars=cluster_data_df.columns[:-1],
var_name='psychometric_test',
value_name='standard deviations from the mean'
)
And here's the graphing code:
(Done with column headings - just note that y-axis=value_name, x-axis = var_name, hue = id_vars):
# plot graph grouped by cluster
sns.set_theme(style="ticks")
fig = plt.figure(figsize=(10, 10))
fig.set(font_scale=1.2)
fig.set_style("white")
# create boxplot
fig.ax = sns.boxplot(y='standard deviations from the mean', x='psychometric_test', hue='cluster', showfliers=False,
data=graph_data)
# set box alpha:
for patch in fig.ax.artists:
r, g, b, a = patch.get_facecolor()
patch.set_facecolor((r, g, b, .2))
# create scatterplot
fig.ax = sns.stripplot(y='standard deviations from the mean', x='psychometric_test', hue='cluster', data=graph_data,
dodge=True, alpha=.25, zorder=1)
# customise legend:
cluster_n: int = dbscan_output.n_clusters
## create list with legend text
i = 0
cluster_info: Dict[int, int] = dbscan_output.cluster_sizes # custom method
legend_labels: List[str] = []
while i < cluster_n:
label: str = f"cluster {i+1}, n = {cluster_info[i]}"
legend_labels.append(label)
i += 1
if -1 in cluster_info.keys():
cluster_n += 1
label: str = f"Unclustered, n = {cluster_info[-1]}"
legend_labels.insert(0, label)
## fetch existing handles and legends (each tuple will have 2*cluster number -> 1 for each boxplot cluster, 1 for each scatterplot cluster, so I will remove the first half)
handles, labels = fig.ax.get_legend_handles_labels()
index: int = int(cluster_n*(-1))
labels = legend_labels
plt.legend(handles[index:], labels[0:])
plt.xticks(rotation=45)
plt.show()
asds
Just a note: Most of my time was spent debugging the melt function. I predominantly got the error "*only integer scalar arrays can be converted to a scalar index with 1D numpy indices array*". My output required me to concatenate my outcome variable value table and the clusters (DBSCAN), and I'd put extra square brackets around the cluster array in the concat method. So I had a column where each value was an invisible List[int], rather than a plain int. It's pretty niche, but maybe it'll help someone.
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