I have a Dataframe with 14 rows and 7 columns where the columns represent groups and the rows represent months. I am trying to create a grouped bar plot such that at each month (on the x-axis) I will have the values for each of the groups as bars. The code is simply
ax = df.plot.bar(width=1,color=['b','g','r','c','orange','purple','y']);
ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
ax.set_xticklabels(months2,rotation=45)
Which produces the following result:
I would like to make the individual bars in each group wider but without them overlapping and I would also like to increase the distance between each group of bars so that there is enough space in the plot.
It might be worth mentioning that the index of the dataframe is 0,...,13.
Help would be greatly appreciated!
TH
If you want to pack 10 apples in a box and want the apples to have more space between them you have two options: (1) take a larger box, or (2) use smaller apples.
(1) How do you change the size of figures drawn with matplotlib?
(2) change the width argument.
Related
I'm working on a bar-plot like this:
I want to move the legend to the upper-left corner and upon the top spine. But it is too high and will covered some bars. So I want to change it as 2 rows and 4 columns. How should I do? I've searched about 1 hour and have no result, maybe I got wrong keywords.
From the fine docs
ncol : integer
The number of columns that the legend has. Default is 1.
enter image description hereI am trying to define the position for a 2 name x-axis label. I have created the graph and would like an x-axis label that says N=5 under the first five bars and 'S' under the 6th bar. Is there a way to do this in python's matplotlib?
My bar plot contains 6 bars with varying eights depending on the feature I am plotting. I have a vertical line separating the last bar as this sample is unique. I am trying to label that last bar with a different x-axis label than the other 5 bars (which should have their own).
The link has my expected outcome. I have two boxes at the bottom for where I would like the x-axis labels demonstrating that the first five samples correspond to one group while the last sample is unique.
I want to create a Pie chart using single column of my dataframe, say my column name is 'Score'. I have stored scores in this column as below :
Score
.92
.81
.21
.46
.72
.11
.89
Now I want to create a pie chart with the range in percentage.
Say 0-0.4 is 30% , 0.4-0.7 is 35 % , 0.7+ is 35% .
I am using the below code using
df1['bins'] = pd.cut(df1['Score'],bins=[0,0.5,1], labels=["0-50%","50-100%"])
df1 = df.groupby(['Score', 'bins']).size().unstack(fill_value=0)
df1.plot.pie(subplots=True,figsize=(8, 3))
With the above code I am getting the Pie chart, but i don’t know how i can do this using percentage.
my pie chart look like this for now
Cutting the dataframe up into bins is the right first step. After which, you can use value_counts with normalize=True in order to get relative frequencies of values in the bins column. This will let you see percentage of data across ranges that are defined in the bins.
In terms of plotting the pie chart, I'm not sure if I understood correctly, but it seemed like you would like to display the correct legend values and the percentage values in each slice of the pie.
pandas.DataFrame.plot is a good place to see all parameters that can be passed into the plot method. You can specify what are your x and y columns to use, and by default, the dataframe index is used as the legend in the pie plot.
To show the percentage values per slice, you can use the autopct parameter as well. As mentioned in this answer, you can use all the normal matplotlib plt.pie() flags in the plot method as well.
Bringing everything together, this is the resultant code and the resultant chart:
df = pd.DataFrame({'Score': [0.92,0.81,0.21,0.46,0.72,0.11,0.89]})
df['bins'] = pd.cut(df['Score'], bins=[0,0.4,0.7,1], labels=['0-0.4','0.4-0.7','0.7-1'], right=True)
bin_percent = pd.DataFrame(df['bins'].value_counts(normalize=True) * 100)
plot = bin_percent.plot.pie(y='bins', figsize=(5, 5), autopct='%1.1f%%')
Plot of Pie Chart
I am trying to build a simple histogram. For some reason, my bars are behaving abnormally. As you can see in this picture, my bar over "3" is moved to the right side. I am not sure what caused it. I did align='mid' but it did not fix it.
This is the code that I used to create it:
def createBarChart(colName):
df[colName].hist(align='mid')
plt.title(str(colName))
RUNS = [1,2,3,4,5]
plt.xticks(RUNS)
plt.show()
for column in colName:
createBarChart(column)
And this is what I got:
bar is not centered over 3
To recreate my data:
df = pd.DataFrame(np.random.randint(1,6,size=(100, 4)), columns=list('ABCD'))
Thank you for your help!
P/s: idk if this info is relevant, but I am using seaborn-whitegrid style. I tried to recreate a plot with sample data and it's still showing up. Is it a bug?
hist created using random data
The hist function is behaving exactly as it is supposed to. By default it splits the data you pass into 10 bins, with the left edge of the first bin at the data's minimum value and the right edge of the last bin at its maximum. The chart below shows the randomly generated data binned this way, with red dashed lines to mark the edges of the bins.
The way around this is to define the bin edges yourself, with a slight adjustment to the minimum and maximum values to centre the bars over the x axis ticks. This can be done quite easily with numpy's linspace function (using column A in the randomly generated data frame as an example):
bins = np.linspace(df["A"].min() - .5, df["A"].max() + .5, 6)
df["A"].hist(bins=bins)
We ask for 6 values because we are defining the bin edges, this will result in 5 bins, as shown in this chart:
If you wanted to keep the gaps between the bars you can increase the number of bins to 9 and adjust the offset slightly, but this wouldn't work in all cases (it works here because every value is either 1, 2, 3, 4 or 5).
bins = np.linspace(df["A"].min() - .25, df["A"].max() + .25, 10)
df["A"].hist(bins=bins)
Finally, as this data contains discrete values and really you are plotting the counts, you could use the value_counts function to create a series that can then be plotted as a bar chart:
df["A"].value_counts().sort_index().plot(kind="bar")
# Provide a 'color' argument if you need all of the bars to look the same.
df["A"].value_counts().sort_index().plot(kind="bar", color="steelblue")
Try using something like this in your code to create all of the histogram bars to the same place.
plt.hist("Your data goes here", bins=range(1,7), align='left', rwidth=1, normed=True)
place your data where I put your data goes here
I'm making a scatter plot from a Pandas DataFrame with 3 columns. The first two would be the x and y axis, and the third would be classicfication data that I want to visualize by points having different colors. My question is, how can I add the legend to this plot:
df= df.groupby(['Month', 'Price'])['Quantity'].sum().reset_index()
df.plot(kind='scatter', x='Month', y='Quantity', c=df.Price , s = 100, legend = True);
As you can see, I'd like to automatically color the dots based on their price, so adding labels manually is a bit of an inconvenience. Is there a way I could add something to this code, that would also show a legend to the Price values?
Also, this colors the scatter plot dots on a range from black to white. Can I add custom colors without giving up the easy usage of c=df.Price?
Thank you!