I have a pandas dataframe:
import pandas as pd
data1 = {'Date':['03-19-2019'],
'Total':[35],
'Solved':[19],
'Arrived':[23],
}
df1 = pd.DataFrame(data1)
and I want to plot a bar plot like this:
with
df1.plot(kind='barh',x='Date',y='Total', ax=ax0, color='#C0C0C0',
width=0.5)
df1.plot(kind='barh',x='Date',y='Arrived', ax=ax0, color='#C0FFFF',
width=0.5)
df1.plot(kind='barh',x='Date',y='Solved', ax=ax0, color='#C0C0FF',
width=0.5)
However, to avoid overlapping, I have to draw each column taking into account which of them has the bigger value.(Total greater than Arrived greater than Solved)
How can I avoid to do this and automate this process easily?
There must be a straightforward and simpler approach in Pandas but I just came up with this quick workaround. The idea is following:
Leave out the first column Date and sort the remaining columns.
Use the sorted indices for plotting the columns in ascending order
To make the colors consistent, you can make use of dictionary so that the ascending/descending order doesn't affect your colors.
fig, ax0 = plt.subplots()
ids = np.argsort(df1.values[0][1:])[::-1]
colors = {'Total': '#C0C0C0', 'Arrived': '#C0FFFF', 'Solved':'#C0C0FF'}
for col in np.array(df1.columns[1:].tolist())[ids]:
df1.plot(kind='barh',x='Date',y=col, ax=ax0, color=colors[col], width=0.1)
A stacked bar graph can be produced in pandas via the stacked=True option. To use this you need to make the "Date" the index first.
import matplotlib.pyplot as plt
import pandas as pd
data1 = {'Date':['03-19-2019'],
'Total':[35],
'Solved':[19],
'Arrived':[23],
}
df = pd.DataFrame(data1)
df.set_index("Date").plot(kind="barh", stacked=True)
plt.show()
Related
I have a multi index dataframe, with the two indices being Sample and Lithology
Sample 20EC-P 20EC-8 20EC-10-1 ... 20EC-43 20EC-45 20EC-54
Lithology Pd Di-Grd Gb ... Hbl Plag Pd Di-Grd Gb
Rb 7.401575 39.055118 6.456693 ... 0.629921 56.535433 11.653543
Ba 24.610102 43.067678 10.716841 ... 1.073115 58.520532 56.946630
Th 3.176471 19.647059 3.647059 ... 0.823529 29.647059 5.294118
I am trying to put it into a seaborn lineplot as such.
spider = sns.lineplot(data = data, hue = data.columns.get_level_values("Lithology"),
style = data.columns.get_level_values("Sample"),
dashes = False, palette = "deep")
The lineplot comes out as
1
I have two issues. First, I want to format hues by lithology and style by sample. Outside of the lineplot function, I can successfully access sample and lithology using data.columns.get_level_values, but in the lineplot they don't seem to do anything and I haven't figured out another way to access these values. Also, the lineplot reorganizes the x-axis by alphabetical order. I want to force it to keep the same order as the dataframe, but I don't see any way to do this in the documentation.
To use hue= and style=, seaborn prefers it's dataframes in long form. pd.melt() will combine all columns and create new columns with the old column names, and a column for the values. The index too needs to be converted to a regular column (with .reset_index()).
Most seaborn functions use order= to set an order on the x-values, but with lineplot the only way is to make the column categorical applying a fixed order.
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
column_tuples = [('20EC-P', 'Pd '), ('20EC-8', 'Di-Grd'), ('20EC-10-1 ', 'Gb'),
('20EC-43', 'Hbl Plag Pd'), ('20EC-45', 'Di-Grd'), ('20EC-54', 'Gb')]
col_index = pd.MultiIndex.from_tuples(column_tuples, names=["Sample", "Lithology"])
data = pd.DataFrame(np.random.uniform(0, 50, size=(3, len(col_index))), columns=col_index, index=['Rb', 'Ba', 'Th'])
data_long = data.melt(ignore_index=False).reset_index()
data_long['index'] = pd.Categorical(data_long['index'], data.index) # make categorical, use order of the original dataframe
ax = sns.lineplot(data=data_long, x='index', y='value',
hue="Lithology", style="Sample", dashes=False, markers=True, palette="deep")
ax.set_xlabel('')
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.02))
plt.tight_layout() # fit legend and labels into the figure
plt.show()
The long dataframe looks like:
index Sample Lithology value
0 Rb 20EC-P Pd 6.135005
1 Ba 20EC-P Pd 6.924961
2 Th 20EC-P Pd 44.270570
...
I was trying to use pd categorical to order the bars in a barplot but the result still didn't get sorted.
import pandas as pd
import numpy as np
np.random.seed(10)
df = pd.DataFrame({'x':np.random.randint(1,10,15),'y': ['x']*15})
df.loc[:,'group'] = df['x'].apply(lambda x:'>=5' if x>=5 else x)
df['group'] = df['group'].astype('string')
sample = df['group'].value_counts().reset_index()
sample['index'] = pd.Categorical(sample['index'],categories=['1','2','3','4','5','6','7','8','9','>=5'], ordered=True)
sample.plot(x='index',kind='bar')
After applied ordered=True, the categories still weren't in order and '>=5' were not at the end of the barplot. Not sure why.
DataFrame.plot.bar() plots the bars in order of occurrence (that is, against the range) and relabel the ticks with the column specified by x.
This is the case even with numerical data:
pd.DataFrame({'idx': [3,2,1], 'val':[4,5,6]}).plot.bar(x='idx')
would give:
In your case, you will need to sort the data before plot:
sample.sort_values('index').plot(x='index',kind='bar')
Output:
I was trying to visualize a facebook stock dataset, where the data for 2014 to 2018 is stored. The dataset looks like this: dataset screenshot
My goal is to visualize the closing column, but by year. That is, year 2014, then 2015 and so on, but they should be in one figure, and one after another. Something like this: expected graph image
But whatever I try, all the graph parts start from index 0, instead of continuing from the end of the previous one. Here's what I got: the graph I generated
Please help me to solve this problem. Thanks!
The most straightforward way is simply to create separate dataframes with empty
values for the non-needed dates.
Here I use an example dataset.
import pandas as pd
import numpy as np
df = pd.DataFrame(
np.random.randint(0, 100, size=100),
index=pd.date_range(start="2020-01-01", periods=100, freq="D"),
)
Then you can create and select the data to plot
df1 = df.copy()
df2 = df.copy()
df1[df.index > pd.to_datetime('2020-02-01')] = np.NaN
df2[df.index < pd.to_datetime('2020-02-01')] = np.NaN
And then simply plot these on the same axis.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(18, 8))
ax.plot(df1)
ax.plot(df2)
The result
I have a dataframe with 3 variables:
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Approved"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
I want a barplot grouped by the Period column, showing all the values ​​contained in the Observations column and colored with the Result column.
How can I do this?
I tried the sns.barplot, but it joined the values in Observations column in just one bar(mean of the values).
sns.barplot(x='Period',y='Observations',hue='Result',data=df,ci=None)
Plot output
Assuming that you want one bar for each row, you can do as follows:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
result_cat = df["Result"].astype("category")
result_codes = result_cat.cat.codes.values
cmap = plt.cm.Dark2(range(df["Result"].unique().shape[0]))
patches = []
for code in result_cat.cat.codes.unique():
cat = result_cat.cat.categories[code]
patches.append(mpatches.Patch(color=cmap[code], label=cat))
df.plot.bar(x='Period',
y='Observations',
color=cmap[result_codes],
legend=False)
plt.ylabel("Observations")
plt.legend(handles=patches)
If you would like it grouped by the months, and then stacked, please use the following (note I updated your code to make sure one month had more than one status), but not sure I completely understood your question correctly:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Under evaluation"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
df.groupby(['Period', 'Result'])['Observations'].sum().unstack('Result').plot(kind='bar', stacked=True)
Given the following:
import numpy as np
import pandas as pd
import seaborn as sns
np.random.seed(365)
x1 = np.random.randn(50)
y1 = np.random.randn(50) * 100
x2 = np.random.randn(50)
y2 = np.random.randn(50) * 100
df1 = pd.DataFrame({'x1':x1, 'y1': y1})
df2 = pd.DataFrame({'x2':x2, 'y2': y2})
sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)
This will create 2 separate plots. How can I add the data from df2 onto the SAME graph? All the seaborn examples I have found online seem to focus on how you can create adjacent graphs (say, via the 'hue' and 'col_wrap' options). Also, I prefer not to use the dataset examples where an additional column might be present as this does not have a natural meaning in the project I am working on.
If there is a mixture of matplotlib/seaborn functions that are required to achieve this, I would be grateful if someone could help illustrate.
You could use seaborn's FacetGrid class to get desired result.
You would need to replace your plotting calls with these lines:
# sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
# sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)
df = pd.concat([df1.rename(columns={'x1':'x','y1':'y'})
.join(pd.Series(['df1']*len(df1), name='df')),
df2.rename(columns={'x2':'x','y2':'y'})
.join(pd.Series(['df2']*len(df2), name='df'))],
ignore_index=True)
pal = dict(df1="red", df2="blue")
g = sns.FacetGrid(df, hue='df', palette=pal, size=5);
g.map(plt.scatter, "x", "y", s=50, alpha=.7, linewidth=.5, edgecolor="white")
g.map(sns.regplot, "x", "y", ci=None, robust=1)
g.add_legend();
This will yield this plot:
Which is if I understand correctly is what you need.
Note that you will need to pay attention to .regplot parameters and may want to change the values I have put as an example.
; at the end of the line is to suppress output of the command (I use ipython notebook where it's visible).
Docs give some explanation on the .map() method. In essence, it does just that, maps plotting command with data. However it will work with 'low-level' plotting commands like regplot, and not lmlplot, which is actually calling regplot behind the scene.
Normally plt.scatter would take parameters: c='none', edgecolor='r' to make non-filled markers. But seaborn is interfering the process and enforcing color to the markers, so I don't see an easy/straigtforward way to fix this, but to manipulate ax elements after seaborn has produced the plot, which is best to be addressed as part of a different question.
Option 1: sns.regplot
In this case, the easiest to implement solution is to use sns.regplot, which is an axes-level function, because this will not require combining df1 and df2.
import pandas as pd
import seaborn
import matplotlib.pyplot as plt
# create the figure and axes
fig, ax = plt.subplots(figsize=(6, 6))
# add the plots for each dataframe
sns.regplot(x='x1', y='y1', data=df1, fit_reg=True, ci=None, ax=ax, label='df1')
sns.regplot(x='x2', y='y2', data=df2, fit_reg=True, ci=None, ax=ax, label='df2')
ax.set(ylabel='y', xlabel='x')
ax.legend()
plt.show()
Option 2: sns.lmplot
As per sns.FacetGrid, it is better to use figure-level functions than to use FacetGrid directly.
Combine df1 and df2 into a long format, and then use sns.lmplot with the hue parameter.
When working with seaborn, it is almost always necessary for the data to be in a long format.
It's customary to use pandas.DataFrame.stack or pandas.melt to convert DataFrames from wide to long.
For this reason, df1 and df2 must have the columns renamed, and have an additional identifying column. This allows them to be concatenated on axis=0 (the default long format), instead of axis=1 (a wide format).
There are a number of ways to combine the DataFrames:
The combination method in the answer from Primer is fine if combining a few DataFrames.
However, a function, as shown below, is better for combining many DataFrames.
def fix_df(data: pd.DataFrame, name: str) -> pd.DataFrame:
"""rename columns and add a column"""
# rename columns to a common name
data.columns = ['x', 'y']
# add an identifying value to use with hue
data['df'] = name
return data
# create a list of the dataframes
df_list = [df1, df2]
# update the dataframes by calling the function in a list comprehension
df_update_list = [fix_df(v, f'df{i}') for i, v in enumerate(df_list, 1)]
# combine the dataframes
df = pd.concat(df_update_list).reset_index(drop=True)
# plot the dataframe
sns.lmplot(data=df, x='x', y='y', hue='df', ci=None)
Notes
Package versions used for this answer:
pandas v1.2.4
seaborn v0.11.1
matplotlib v3.3.4