Use these commands to install matplotlib, pandas, and numpy:
pip install matplotlib pip install pandas pip install numpy
Parcel types:
- Basic plotting. In this basic plotting, we can use randomly generated data to plot using series and matplotlib.
# import libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
ts
=
pd.Series (np.random.randn (
1000
), in dex
=
pd.date_range (
’1/1 / 2000’
, periods
=
1000
))
ts
=
ts.cumsum ()
ts.plot ()
plt.show ()
Output:
- Different data plot: using more than one list of data in a plot.
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
ts
=
pd.Series ( np.random.randn (
1000
), index
=
pd.date_range (
’1/1 / 2000’
, periods
=
1000
))
df
=
pd.DataFrame (np.random.randn (
1000
,
4
),
index
=
ts.index, columns
=
list
(
’ABCD’
))
df
=
df.cumsum ()
plt.figure ()
df .plot ()
plt.show ()
Output:
, Li" - Plot on a given axis: we can explicitly define an axis name and plot data based on that axis.
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
ts
=
pd.Series (np .random.randn (
1000
), index
=
pd.date_range (
’1/1/2000 ’
, periods
=
1000
))
df
=
pd.DataFrame (np .random.randn (
1000
,
4
), index
=
ts.index,
columns
=
list
(
’ABCD’
))
df3
=
pd.DataFrame (np.random.randn (
1000
,
2
),
columns
=
[
’B’
,
’ C’
]). cumsu m ()
df3 [
’A’
]
=
pd.Series (
list
(
range
(
len
(df))))
df3.plot (x
=
’ A’
, y
=
’B’
)
plt.show ()
Output:
- Histogram using matplotlib: Look for different types of histogram to clearly understand the behavior of the data.
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
ts
=
pd.Series (np.random.randn (
1000
), index
=
pd.date_range (
’1/1 / 2000’
, periods
=
1000
))
df
=
pd.DataFrame (np.random.randn (
1000
,
4
), index
=
ts.index,
columns
=
list
(
’ ABCD’
))
df3
=
pd.DataFrame (np.random.randn (
1000
,
2
),
columns
=
[
’B’
,
’ C’
]). cumsum ()
df3 [
’A’
]
=
pd.Series (
list
(
range
(
len
(df))))
df3.iloc [
5
]. plot.bar ()
plt.axhline (
0
, color
=
’k ’
)
plt.show ()
Output:
- Histograms:
# import libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
df4
=
pd.DataFrame ({
’a’
: np.random.randn (
1000
)
+
1
,
’b’
: np.random.randn (
1000
),
’c’
: np.random.randn (
1000
)
-
1
},
columns
=
[
’ a’
,
’b’
,
’c’
])
plt.figure ()
df4.plot.hist (alpha
=
0.5
)
plt.show ()
Exit:
- Plot using Series and matplotlib: use a block to plot the data.
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
df
=
pd .DataFrame (np.random.rand (
10
,
5
),
columns
=
[
’A’
,
’B’
,
’ C’
,
’ D’
,
’E’
])
df.plot .box ()
plt.show ()
Output:
- Plot density:
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
df
=
pd.DataFrame (np.random.rand (
10
,
5
),
columns
=
[
’A’
,
’ B’
,
’C’
,
’D’
,
’ E’
])
ser
=
pd.Series (np.random.randn (
1000 ))
ser.plot.kde ()
plt.show ()
Output:
- Plot area using matplotlib:
# library import
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
df
=
pd.DataFrame (np.random.ran d (
10
,
5
),
columns
=
[
’A’
,
’B’
,
’ C’
,
’D’
,
’ E’
])
df.plot.area ()
plt.show ()
Output:
- Dot plot:
# importing libraries
import
matplotlib .pyplot as plt
import
pandas as pd
import
numpy as np
df
=
pd.DataFrame (np.random.rand (
500
,
4
),
columns
=
[
’a’
,
’b’
,
’ c’
,
’d’
])
df.plot.scatter (x
=
’a’
, y
=
’ b’
)
plt.show ()
Output:
- Hexagonal bunker
# importing libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
df
=
pd.DataFrame (np.random.randn (
1000
,
2
) , columns
=
[
’a’
,
’b’
])
df [
’a’
]
=
df [
’ a’
]
+
np.arange (
1000
)
df.plot.hexbin (x
=
’ a’
, y
=
’b’
, gridsize
=
25
)
plt.show ()
Output:
- Circular plot:
# import libraries
import
matplotlib.pyplot as plt
import
pandas as pd
import
numpy as np
series
=
pd.Series (
3
*
np.random.rand (
4
) ,
index
=
[
’a’
,
’b’
,
’ c’
,
’d’
], name
=
’ series’
)
series.plot.pie (figsize
=
(
4
,
4
))
plt.show ()
Output: