How to plot frequency count of pandas column?
I have a pandas dataframe like this:
Year Winner
4 1954 Germany
9 1974 Germany
13 1990 Germany
19 2014 Germany
5 1958 Brazil
6 1962 Brazil
8 1970 Brazil
14 1994 Brazil
16 2002 Brazil
How to plot the frequency count of column Winner, so that y axis has frequency and x-axis has name of country?
I tried:
import numpy as np
import pandas as pd
df.groupby('Winner').size().plot.hist()
df1['Winner'].value_counts().plot.hist()
python pandas
asked Dec 28 '18 at 5:05
astro123astro123
1876
add a comment |
I have a pandas dataframe like this:
Year Winner
4 1954 Germany
9 1974 Germany
13 1990 Germany
19 2014 Germany
5 1958 Brazil
6 1962 Brazil
8 1970 Brazil
14 1994 Brazil
16 2002 Brazil
How to plot the frequency count of column Winner, so that y axis has frequency and x-axis has name of country?
I tried:
import numpy as np
import pandas as pd
df.groupby('Winner').size().plot.hist()
df1['Winner'].value_counts().plot.hist()
python pandas
asked Dec 28 '18 at 5:05
astro123astro123
1876
add a comment |
I have a pandas dataframe like this:
Year Winner
4 1954 Germany
9 1974 Germany
13 1990 Germany
19 2014 Germany
5 1958 Brazil
6 1962 Brazil
8 1970 Brazil
14 1994 Brazil
16 2002 Brazil
How to plot the frequency count of column Winner, so that y axis has frequency and x-axis has name of country?
I tried:
import numpy as np
import pandas as pd
df.groupby('Winner').size().plot.hist()
df1['Winner'].value_counts().plot.hist()
python pandas
asked Dec 28 '18 at 5:05
astro123astro123
1876
I have a pandas dataframe like this:
Year Winner
4 1954 Germany
9 1974 Germany
13 1990 Germany
19 2014 Germany
5 1958 Brazil
6 1962 Brazil
8 1970 Brazil
14 1994 Brazil
16 2002 Brazil
How to plot the frequency count of column Winner, so that y axis has frequency and x-axis has name of country?
I tried:
import numpy as np
import pandas as pd
df.groupby('Winner').size().plot.hist()
df1['Winner'].value_counts().plot.hist()
python pandas
python pandas
asked Dec 28 '18 at 5:05
astro123astro123
1876
asked Dec 28 '18 at 5:05
astro123astro123
1876
asked Dec 28 '18 at 5:05
astro123astro123
1876
asked Dec 28 '18 at 5:05
astro123astro123
1876
asked Dec 28 '18 at 5:05
astro123astro123
1876
1876
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You are close, need Series.plot.bar because value_counts already count frequency:
df1['Winner'].value_counts().plot.bar()
Also working:
df1.groupby('Winner').size().plot.bar()
Difference between solutions is output of value_counts will be in descending order so that the first element is the most frequently-occurring element.
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
1
I did thisdf1.set_index(df1.Year)['Winner'].value_counts().plot.bar();
– astro123
Dec 28 '18 at 5:33
add a comment |
In addition to @jezrael's answer, you can also do:
df1['Winner'].value_counts().plot(kind='bar')
Other one from @jezrael could be:
df1.groupby('Winner').size().plot(kind='bar')
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
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 are close, need Series.plot.bar because value_counts already count frequency:
df1['Winner'].value_counts().plot.bar()
Also working:
df1.groupby('Winner').size().plot.bar()
Difference between solutions is output of value_counts will be in descending order so that the first element is the most frequently-occurring element.
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
1
I did thisdf1.set_index(df1.Year)['Winner'].value_counts().plot.bar();
– astro123
Dec 28 '18 at 5:33
add a comment |
You are close, need Series.plot.bar because value_counts already count frequency:
df1['Winner'].value_counts().plot.bar()
Also working:
df1.groupby('Winner').size().plot.bar()
Difference between solutions is output of value_counts will be in descending order so that the first element is the most frequently-occurring element.
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
1
I did thisdf1.set_index(df1.Year)['Winner'].value_counts().plot.bar();
– astro123
Dec 28 '18 at 5:33
add a comment |
You are close, need Series.plot.bar because value_counts already count frequency:
df1['Winner'].value_counts().plot.bar()
Also working:
df1.groupby('Winner').size().plot.bar()
Difference between solutions is output of value_counts will be in descending order so that the first element is the most frequently-occurring element.
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
You are close, need Series.plot.bar because value_counts already count frequency:
df1['Winner'].value_counts().plot.bar()
Also working:
df1.groupby('Winner').size().plot.bar()
Difference between solutions is output of value_counts will be in descending order so that the first element is the most frequently-occurring element.
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
edited Dec 28 '18 at 5:17
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
answered Dec 28 '18 at 5:07
jezraeljezrael
322k23265342
322k23265342
1
I did thisdf1.set_index(df1.Year)['Winner'].value_counts().plot.bar();
– astro123
Dec 28 '18 at 5:33
add a comment |
1
I did thisdf1.set_index(df1.Year)['Winner'].value_counts().plot.bar();
– astro123
Dec 28 '18 at 5:33
1
1
I did this
df1.set_index(df1.Year)['Winner'].value_counts().plot.bar();– astro123
Dec 28 '18 at 5:33
I did this
df1.set_index(df1.Year)['Winner'].value_counts().plot.bar();– astro123
Dec 28 '18 at 5:33
add a comment |
In addition to @jezrael's answer, you can also do:
df1['Winner'].value_counts().plot(kind='bar')
Other one from @jezrael could be:
df1.groupby('Winner').size().plot(kind='bar')
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
add a comment |
In addition to @jezrael's answer, you can also do:
df1['Winner'].value_counts().plot(kind='bar')
Other one from @jezrael could be:
df1.groupby('Winner').size().plot(kind='bar')
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
add a comment |
In addition to @jezrael's answer, you can also do:
df1['Winner'].value_counts().plot(kind='bar')
Other one from @jezrael could be:
df1.groupby('Winner').size().plot(kind='bar')
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
In addition to @jezrael's answer, you can also do:
df1['Winner'].value_counts().plot(kind='bar')
Other one from @jezrael could be:
df1.groupby('Winner').size().plot(kind='bar')
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
edited Dec 28 '18 at 5:23
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
answered Dec 28 '18 at 5:10
U9-ForwardU9-Forward
13.3k21237
13.3k21237
add a comment |
add a comment |
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