I am trying to plot a line graph from several columns
ax = sns.lineplot(data=mt,
x= ['pt'],
y = [c for c in mt.columns if c not in ['pt']],
dashes=False)
The response I am getting is
ValueError: Length of list vectors must match length of `data` when both are used, but `data` has length 13 and the vector passed to `x` has length 1.
Seaborn's prefers data in long form, which can be created via pd.melt(). A wide form dataframe is supported if you create an index (and the data isn't too complex).
Here is a simple example:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
mt = pd.DataFrame({'pt': np.arange(100),
'y1': np.random.randn(100).cumsum(),
'y2': np.random.randn(100).cumsum(),
'y3': np.random.randn(100).cumsum()})
sns.set()
ax = sns.lineplot(data=mt.set_index('pt'), dashes=True)
plt.tight_layout()
plt.show()
Related
I have a dataframe where each column has many missing values. How can I make a plot where the datapoints in each column are joined with lines, i.e. NAs are ignored, instead of having a choppy plot?
import numpy as np
import pandas as pd
pd.options.plotting.backend = "plotly"
d = pd.DataFrame(data = np.random.choice([np.nan] + list(range(7)), size=(10,3)))
d.plot(markers=True)
One way is to use this for each column:
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name="linear",
line_shape='linear'))
Are there any better ways to accomplish this?
You can use pandas interpolate. Have demonstrated using plotly express and chained use so underlying data is not changed.
Post comments have amended answer so that markers are not shown for interpreted points.
import numpy as np
import pandas as pd
import plotly.express as px
d = pd.DataFrame(data=np.random.choice([np.nan] + list(range(7)), size=(10, 3)))
px.line(d).update_traces(mode="lines+markers").add_traces(
px.line(d.interpolate(limit_direction="both")).update_traces(showlegend=False).data
)
How would I assign markers of different symbols to each of the max values found in each curve?, Ie, 4 different markers showing the max value in each curve.
Here is my attempt
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df = pd.DataFrame(np.random.randint(0,1000,size=(100, 4)), columns=list('ABCD'))
maxValues=df.max()
m=['o', '.', ',', 'x',]
df.plot()
plt.plot(maxValues, marker=m)
In my real df, the number of columns will vary.
You can do it this way. Note that I used a V instead of , as the comma (pixel) wasn't showing up clearly.
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df = pd.DataFrame(np.random.randint(0,1000,size=(100, 4)), columns=list('ABCD'))
df.plot(figsize=(20,5))
mrk = pd.DataFrame({'A': [df[['A']].idxmax()[0], df['A'].max(), 'o'],
'B': [df[['B']].idxmax()[0], df['B'].max(), '.'],
'C': [df[['C']].idxmax()[0], df['C'].max(), 'v'],
'D': [df[['D']].idxmax()[0], df['D'].max(), 'x']})
for col in range(len(mrk.columns)):
plt.plot(mrk.iloc[0,col], mrk.iloc[1, col], marker=mrk.iloc[2, col], markersize=20)
I created the mrk dataframe manually as it was small, but you can use loops to go through the various columns in your real data. The graph looks like this. Adjust markersize to increase/decrease size of the markers.
I am trying to do a catplot using seaborn library for all the categorical variables in my dataframe but I ma getting error for ambiguous truth value. It generally happens with "&" value but I am unable to get the root cause here. My target is continuous variable.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
target = df[target_col]
features = df[df.columns.difference([target_col])]
cat_cols = features.select_dtypes(include=['object']).columns.to_list()
fig, axes = plt.subplots(round(len(cat_cols) / 3), 3, figsize=(15, 15))
for i, ax in enumerate(fig.axes):
if i < len(cat_cols):
sns.catplot(x=cat_cols[i], y=target, kind='bar',data=df, ax = ax)
But I am getting the below error. Which part is causing this value error?
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
sns.catplot is a grid level plot, so you should not slot it into a subplot. You can use a facetgrid with barplot:
For example this is your data:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({'y':np.random.uniform(0,1,50),'A':np.random.choice(['a1','a2'],50),
'B':np.random.choice(['b1','b2'],50),'C':np.random.randint(0,10,50),
'D':np.random.choice(['d1','d2'],50),'E':np.random.choice(['e1','e2'],50)})
target_col = "y"
cat_cols = df.columns[df.dtypes==object]
seaborn works better with long format, so you can pivot your data long like this:
df.melt(id_vars=target_col,value_vars=cat_cols)
y variable value
0 0.606734 A a1
1 0.603324 A a2
2 0.938280 A a2
3 0.718703 A a1
4 0.808013 A a1
The column variable now defines the facet to plot and the x-axis is your value. We call this directly:
g = sns.FacetGrid(df.melt(id_vars=target_col,value_vars=cat_cols),
col='variable', sharex=False,col_wrap=3)
g.map_dataframe(sns.barplot, x="value", y="y")
I am creating probability distributions for each column of my data frame by distplot from seaborn library sns.distplot(). For one plot I do
x = df['A']
sns.distplot(x);
I am trying to use the FacetGrid & Map to have all plots for each columns at once
in this way. But doesn't work at all.
g = sns.FacetGrid(df, col = 'A','B','C','D','E')
g.map(sns.distplot())
I think you need to use melt to reshape your dataframe to long format, see this MVCE:
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
dfm = df.melt(var_name='columns')
g = sns.FacetGrid(dfm, col='columns')
g = (g.map(sns.distplot, 'value'))
Output:
From seaborn 0.11.2 it is not recommended to use FacetGrid directly. Instead, use sns.displot for figure-level plots.
np.random.seed(2022)
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
dfm = df.melt(var_name='columns')
g = sns.displot(data=dfm, x='value', col='columns', col_wrap=3, common_norm=False, kde=True, stat='density')
You're getting this wrong on two levels.
Python syntax.
FacetGrid(df, col = 'A','B','C','D','E') is invalid, because col gets set to A and the remaining characters are interpreted as further arguments. But since they are not named, this is invalid python syntax.
Seaborn concepts.
Seaborn expects a single column name as input for the col or row argument. This means that the dataframe needs to be in a format that has one column which determines to which column or row the respective datum belongs.
You do not call the function to be used by map. The idea is of course that map itself calls it.
Solutions:
Loop over columns:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
fig, axes = plt.subplots(ncols=5)
for ax, col in zip(axes, df.columns):
sns.distplot(df[col], ax=ax)
plt.show()
Melt dataframe
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(np.random.randn(14,5), columns=list("ABCDE"))
g = sns.FacetGrid(df.melt(), col="variable")
g.map(sns.distplot, "value")
plt.show()
You can use the following:
# listing dataframes types
list(set(df.dtypes.tolist()))
# include only float and integer
df_num = df.select_dtypes(include = ['float64', 'int64'])
# display what has been selected
df_num.head()
# plot
df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);
I think the easiest approach is to just loop the columns and create a plot.
import numpy as np
improt pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.random((100,5)), columns = list('ABCDE'))
for col in df.columns:
hist = df[col].hist(bins=10)
print("Plotting for column {}".format(col))
plt.show()
My DataFrame's structure
trx.columns
Index(['dest', 'orig', 'timestamp', 'transcode', 'amount'], dtype='object')
I'm trying to plot transcode (transaction code) against amount to see the how much money is spent per transaction. I made sure to convert transcode to a categorical type as seen below.
trx['transcode']
...
Name: transcode, Length: 21893, dtype: category
Categories (3, int64): [1, 17, 99]
The result I get from doing plt.scatter(trx['transcode'], trx['amount']) is
Scatter plot
While the above plot is not entirely wrong, I would like the X axis to contain just the three possible values of transcode [1, 17, 99] instead of the entire [1, 100] range.
Thanks!
In matplotlib 2.1 you can plot categorical variables by using strings. I.e. if you provide the column for the x values as string, it will recognize them as categories.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"x" : np.random.choice([1,17,99], size=100),
"y" : np.random.rand(100)*100})
plt.scatter(df["x"].astype(str), df["y"])
plt.margins(x=0.5)
plt.show()
In order to optain the same in matplotlib <=2.0 one would plot against some index instead.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"x" : np.random.choice([1,17,99], size=100),
"y" : np.random.rand(100)*100})
u, inv = np.unique(df["x"], return_inverse=True)
plt.scatter(inv, df["y"])
plt.xticks(range(len(u)),u)
plt.margins(x=0.5)
plt.show()
The same plot can be obtained using seaborn's stripplot:
sns.stripplot(x="x", y="y", data=df)
And a potentially nicer representation can be done via seaborn's swarmplot:
sns.swarmplot(x="x", y="y", data=df)