Overlay two separate histograms in python
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I have two separate dataframes that I made into histograms and I want to know how I can overlay them so for each category in the x axis the bar is a different color for each dataframe. This is the code I have for the separate bar graphs.
df1.plot.bar(x='brand', y='desc')
df2.groupby(['brand']).count()['desc'].plot(kind='bar')
I tried this code:
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc'].plot(kind='bar')
bins = np.linspace(1, 4)
plt.hist(x, bins, alpha=0.9,normed=1, label='Previous')
plt.hist(y, bins, alpha=0.5, normed=0,label='Current')
plt.legend(loc='upper right')
plt.show()
This code is not overlaying the graphs properly. The problem is dataframe 2 doesn't have numeric values so i need to use the count method. Appreciate the help!
python dataframe matplotlib
add a comment |
I have two separate dataframes that I made into histograms and I want to know how I can overlay them so for each category in the x axis the bar is a different color for each dataframe. This is the code I have for the separate bar graphs.
df1.plot.bar(x='brand', y='desc')
df2.groupby(['brand']).count()['desc'].plot(kind='bar')
I tried this code:
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc'].plot(kind='bar')
bins = np.linspace(1, 4)
plt.hist(x, bins, alpha=0.9,normed=1, label='Previous')
plt.hist(y, bins, alpha=0.5, normed=0,label='Current')
plt.legend(loc='upper right')
plt.show()
This code is not overlaying the graphs properly. The problem is dataframe 2 doesn't have numeric values so i need to use the count method. Appreciate the help!
python dataframe matplotlib
Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32
add a comment |
I have two separate dataframes that I made into histograms and I want to know how I can overlay them so for each category in the x axis the bar is a different color for each dataframe. This is the code I have for the separate bar graphs.
df1.plot.bar(x='brand', y='desc')
df2.groupby(['brand']).count()['desc'].plot(kind='bar')
I tried this code:
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc'].plot(kind='bar')
bins = np.linspace(1, 4)
plt.hist(x, bins, alpha=0.9,normed=1, label='Previous')
plt.hist(y, bins, alpha=0.5, normed=0,label='Current')
plt.legend(loc='upper right')
plt.show()
This code is not overlaying the graphs properly. The problem is dataframe 2 doesn't have numeric values so i need to use the count method. Appreciate the help!
python dataframe matplotlib
I have two separate dataframes that I made into histograms and I want to know how I can overlay them so for each category in the x axis the bar is a different color for each dataframe. This is the code I have for the separate bar graphs.
df1.plot.bar(x='brand', y='desc')
df2.groupby(['brand']).count()['desc'].plot(kind='bar')
I tried this code:
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc'].plot(kind='bar')
bins = np.linspace(1, 4)
plt.hist(x, bins, alpha=0.9,normed=1, label='Previous')
plt.hist(y, bins, alpha=0.5, normed=0,label='Current')
plt.legend(loc='upper right')
plt.show()
This code is not overlaying the graphs properly. The problem is dataframe 2 doesn't have numeric values so i need to use the count method. Appreciate the help!
python dataframe matplotlib
python dataframe matplotlib
asked Nov 15 '18 at 14:17
Py.rookie89Py.rookie89
277
277
Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32
add a comment |
Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32
Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32
Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32
add a comment |
2 Answers
2
active
oldest
votes
You might have to use axes objects in matplotlib. In simple terms, you create a figure with some axes object associated with it, then you can call hist from it. Here's one way you can do it:
fig, ax = plt.subplots(1, 1)
ax.hist(x, bins, alpha=0.9,normed=1, label='Previous')
ax.hist(y, bins, alpha=0.5, normed=0,label='Current')
ax.legend(loc='upper right')
plt.show()
add a comment |
Make use of seaborn's histogram with several variables. In your case it would be:
import seaborn as sns
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc']
sns.distplot( previous , color="skyblue", label="previous")
sns.distplot( current , color="red", label="Current")
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You might have to use axes objects in matplotlib. In simple terms, you create a figure with some axes object associated with it, then you can call hist from it. Here's one way you can do it:
fig, ax = plt.subplots(1, 1)
ax.hist(x, bins, alpha=0.9,normed=1, label='Previous')
ax.hist(y, bins, alpha=0.5, normed=0,label='Current')
ax.legend(loc='upper right')
plt.show()
add a comment |
You might have to use axes objects in matplotlib. In simple terms, you create a figure with some axes object associated with it, then you can call hist from it. Here's one way you can do it:
fig, ax = plt.subplots(1, 1)
ax.hist(x, bins, alpha=0.9,normed=1, label='Previous')
ax.hist(y, bins, alpha=0.5, normed=0,label='Current')
ax.legend(loc='upper right')
plt.show()
add a comment |
You might have to use axes objects in matplotlib. In simple terms, you create a figure with some axes object associated with it, then you can call hist from it. Here's one way you can do it:
fig, ax = plt.subplots(1, 1)
ax.hist(x, bins, alpha=0.9,normed=1, label='Previous')
ax.hist(y, bins, alpha=0.5, normed=0,label='Current')
ax.legend(loc='upper right')
plt.show()
You might have to use axes objects in matplotlib. In simple terms, you create a figure with some axes object associated with it, then you can call hist from it. Here's one way you can do it:
fig, ax = plt.subplots(1, 1)
ax.hist(x, bins, alpha=0.9,normed=1, label='Previous')
ax.hist(y, bins, alpha=0.5, normed=0,label='Current')
ax.legend(loc='upper right')
plt.show()
answered Nov 16 '18 at 16:18
Luis OrtizLuis Ortiz
414
414
add a comment |
add a comment |
Make use of seaborn's histogram with several variables. In your case it would be:
import seaborn as sns
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc']
sns.distplot( previous , color="skyblue", label="previous")
sns.distplot( current , color="red", label="Current")
add a comment |
Make use of seaborn's histogram with several variables. In your case it would be:
import seaborn as sns
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc']
sns.distplot( previous , color="skyblue", label="previous")
sns.distplot( current , color="red", label="Current")
add a comment |
Make use of seaborn's histogram with several variables. In your case it would be:
import seaborn as sns
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc']
sns.distplot( previous , color="skyblue", label="previous")
sns.distplot( current , color="red", label="Current")
Make use of seaborn's histogram with several variables. In your case it would be:
import seaborn as sns
previous = df1.plot.bar(x='brand', y='desc')
current= df2.groupby(['brand']).count()['desc']
sns.distplot( previous , color="skyblue", label="previous")
sns.distplot( current , color="red", label="Current")
answered Nov 16 '18 at 16:35
SantiStSuperySantiStSupery
117112
117112
add a comment |
add a comment |
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Is the shown code supposed to be a Minimal, Complete, and Verifiable example demonstrating your problem?
– Yunnosch
Nov 15 '18 at 14:32