I am trying to plot some results obtained after optimisation using Gurobi.
I have converted the dictionary to python dataframe.
it is 96*1
But now how do I use this dataframe to plot as 1st row-value, 2nd row-value, I am attaching the snapshot of the same.
Please anyone can help me in this?
x={}
for t in time1:
x[t]= [price_energy[t-1]*EnergyResource[174,t].X]
df = pd.DataFrame.from_dict(x, orient='index')
df
You can try pandas.DataFrame(data=x.values()) to properly create a pandas DataFrame while using row numbers as indices.
In the example below, I have generated a (pseudo) random dictionary with 10 values, and stored it as a data frame using pandas.DataFrame giving a name to the only column as xyz. To understand how indexing works, please see Indexing and selecting data.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Create a dictionary 'x'
rng = np.random.default_rng(121)
x = dict(zip(np.arange(10), rng.random((1, 10))[0]))
# Create a dataframe from 'x'
df = pd.DataFrame(x.values(), index=x.keys(), columns=["xyz"])
print(df)
print(df.index)
# Plot the dataframe
plt.plot(df.index, df.xyz)
plt.show()
This prints df as:
xyz
0 0.632816
1 0.297902
2 0.824260
3 0.580722
4 0.593562
5 0.793063
6 0.444513
7 0.386832
8 0.214222
9 0.029993
and gives df.index as:
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
and also plots the figure:
Related
I have a DataFrame with two pandas Series as follow:
value accepted_values
0 1 [1, 2, 3, 4]
1 2 [5, 6, 7, 8]
I would like to efficiently check if the value is in accepted_values using pandas methods.
I already know I can do something like the following, but I'm interested in a faster approach if there is one (took around 27 seconds on 1 million rows DataFrame)
import pandas as pd
df = pd.DataFrame({"value":[1, 2], "accepted_values": [[1,2,3,4], [5, 6, 7, 8]]})
def check_first_in_second(values: pd.Series):
return values[0] in values[1]
are_in_accepted_values = df[["value", "accepted_values"]].apply(
check_first_in_second, axis=1
)
if not are_in_accepted_values.all():
raise AssertionError("Not all value in accepted_values")
I think if create DataFrame with list column you can compare by DataFrame.eq and test if match at least one value per row by DataFrame.any:
df1 = pd.DataFrame(df["accepted_values"].tolist(), index=df.index)
are_in_accepted_values = df1.eq(df["value"]).any(axis=1).all()
Another idea:
are_in_accepted_values = all(v in a for v, a in df[["value", "accepted_values"]].to_numpy())
I found a little optimisation to your second idea. Using a bit more numpy than pandas makes it faster (more than 3x, tested with time.perf_counter()).
values = df["value"].values
accepted_values = df["accepted_values"].values
are_in_accepted_values = all(s in e for s, e in np.column_stack([values, accepted_values]))
I have a pandas DataFrame, which contains 610 rows, and every row contains a nested list of coordinate pairs, it looks like that:
[1377778.4800000004, 6682395.377599999] is one coordinate pair.
I want to unnest every row, so instead of one row containing a list of coordinates I will have one row for every coordinate pair, i.e.:
I've tried s.apply(pd.Series).stack() from this question Split nested array values from Pandas Dataframe cell over multiple rows but unfortunately that didn't work.
Please any ideas? Many thanks in advance!
Here my new answer to your problem. I used "reduce" to flatten your nested array and then I used "itertools chain" to turn everything into a 1d list. After that I reshaped the list into a 2d array which allows you to convert it to the dataframe that you need. I tried to be as generic as possible. Please let me know if there are any problems.
#libraries
import operator
from functools import reduce
from itertools import chain
#flatten lists of lists using reduce. Then turn everything into a 1d list using
#itertools chain.
reduced_coordinates = list(chain.from_iterable(reduce(operator.concat,
geometry_list)))
#reshape the coordinates 1d list to a 2d and convert it to a dataframe
df = pd.DataFrame(np.reshape(reduced_coordinates, (-1, 2)))
df.columns = ['X', 'Y']
One thing you can do is use numpy. It allows you to perform a lot of list/ array operations in a fast and efficient way. This includes "unnesting" (reshaping) lists. Then you only have to convert to pandas dataframe.
For example,
import numpy as np
#your list
coordinate_list = [[[1377778.4800000004, 6682395.377599999],[6582395.377599999, 2577778.4800000004], [6582395.377599999, 2577778.4800000004]]]
#convert list to array
coordinate_array = numpy.array(coordinate_list)
#print shape of array
coordinate_array.shape
#reshape array into pairs of
reshaped_array = np.reshape(coordinate_array, (3, 2))
df = pd.DataFrame(reshaped_array)
df.columns = ['X', 'Y']
The output will look like this. Let me know if there is something I am missing.
import pandas as pd
import numpy as np
data = np.arange(500).reshape([250, 2])
cols = ['coord']
new_data = []
for item in data:
new_data.append([item])
df = pd.DataFrame(data=new_data, columns=cols)
print(df.head())
def expand(row):
row['x'] = row.coord[0]
row['y'] = row.coord[1]
return row
df = df.apply(expand, axis=1)
df.drop(columns='coord', inplace=True)
print(df.head())
RESULT
coord
0 [0, 1]
1 [2, 3]
2 [4, 5]
3 [6, 7]
4 [8, 9]
x y
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
I want reshape my data vector, but when I running the code
from pandas import read_csv
import numpy as np
#from pandas import Series
#from matplotlib import pyplot
series =read_csv('book1.csv', header=0, parse_dates=[0], index_col=0, squeeze=True)
A= np.array(series)
B = np.reshape(10,10)
print (B)
I found error
result = getattr(asarray(obj), method)(*args, **kwds)
ValueError: total size of new array must be unchanged
my data
Month xxx
1749-01 58
1749-02 62.6
1749-03 70
1749-04 55.7
1749-05 85
1749-06 83.5
1749-07 94.8
1749-08 66.3
1749-09 75.9
1749-10 75.5
1749-11 158.6
1749-12 85.2
1750-01 73.3
.... ....
.... ....
There seem to be two issues with what you are trying to do. The first relates to how you read the data in pandas:
series = read_csv('book1.csv', header=0, parse_dates=[0], index_col=0, squeeze=True)
print(series)
>>>>Empty DataFrame
Columns: []
Index: [1749-01 58, 1749-02 62.6, 1749-03 70, 1749-04 55.7, 1749-05 85, 1749-06 83.5, 1749-07 94.8, 1749-08 66.3, 1749-09 75.9, 1749-10 75.5, 1749-11 158.6, 1749-12 85.2, 1750-01 73.3]
This isn't giving you a column of floats in a dataframe with the dates the index, it is putting each line into the index, dates and value. I would think that you want to add delimtier=' ' so that it splits the lines properly:
series =read_csv('book1.csv', header=0, parse_dates=[0], index_col=0, delimiter=' ', squeeze=True)
>>>> Month
1749-01-01 58.0
1749-02-01 62.6
1749-03-01 70.0
1749-04-01 55.7
1749-05-01 85.0
1749-06-01 83.5
1749-07-01 94.8
1749-08-01 66.3
1749-09-01 75.9
1749-10-01 75.5
1749-11-01 158.6
1749-12-01 85.2
1750-01-01 73.3
Name: xxx, dtype: float64
This gives you the dates as the index with the 'xxx' value in the column.
Secondly the reshape. The error is quite descriptive in this case. If you want to use numpy.reshape you can't reshape to a layout that has a different number of elements to the original data. For example:
import numpy as np
a = np.array([1, 2, 3, 4, 5, 6]) # size 6 array
a.reshape(2, 3)
>>>> [[1, 2, 3],
[4, 5, 6]]
This is fine because the array starts out length 6, and I'm reshaping to 2 x 3, and 2 x 3 = 6.
However, if I try:
a.reshape(10, 10)
>>>> ValueError: cannot reshape array of size 6 into shape (10,10)
I get the error, because I need 10 x 10 = 100 elements to do this reshape, and I only have 6.
Without the complete dataset it's impossible to know for sure, but I think this is the same problem you are having, although you are converting your whole dataframe to a numpy array.
I have a dataframe:
a b c
0 1 2 3
1 1 1 1
2 3 7 NaN
3 2 3 5
...
I want to fill column "three" inplace (update the values) where the values are NaN using a machine learning algorithm.
I don't know how to do it inplace. Sample code:
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
df=pd.DataFrame([range(3), [1, 5, np.NaN], [2, 2, np.NaN], [4,5,9], [2,5,7]],columns=['a','b','c'])
x=[]
y=[]
for row in df.iterrows():
index,data = row
if(not pd.isnull(data['c'])):
x.append(data[['a','b']].tolist())
y.append(data['c'])
model = LinearRegression()
model.fit(x,y)
#this line does not do it in place.
df[~df.c.notnull()].assign(c = lambda x:model.predict(x[['a','b']]))
But this gives me a copy of the dataframe. Only option I have left is using a for loop however, I don't want to do that. I think there should be more pythonic way of doing it using pandas. Can someone please help? Or is there any other way of doing this?
You'll have to do something like :
df.loc[pd.isnull(df['three']), 'three'] = _result of model_
This modifies directly dataframe df
This way you first filter the dataframe to keep the slice you want to modify (pd.isnull(df['three'])), then from that slice you select the column you want to modify (three).
On the right hand side of the equal, it expects to get an array / list / series with the same number of lines than the filtered dataframe ( in your example, one line)
You may have to adjust depending on what your model returns exactly
EDIT
You probably need to do stg like this
pred = model.predict(df[['a', 'b']])
df['pred'] = model.predict(df[['a', 'b']])
df.loc[pd.isnull(df['c']), 'c'] = df.loc[pd.isnull(df['c']), 'pred']
Note that a significant part of the issue comes from the way you are using scikit learn in your example. You need to pass the whole dataset to the model when you predict.
The simplest way is yo transpose first, then forward fill/backward fill at your convenience.
df.T.ffill().bfill().T
I want to visualize my data into box plots that are grouped by another variable shown here in my terrible drawing:
So what I do is to use a pandas series variable to tell pandas that I have grouped variables so this is what I do:
import pandas as pd
import seaborn as sns
#example data for reproduciblity
a = pd.DataFrame(
[
[2, 1],
[4, 2],
[5, 1],
[10, 2],
[9, 2],
[3, 1]
])
#converting second column to Series
a.ix[:,1] = pd.Series(a.ix[:,1])
#Plotting by seaborn
sns.boxplot(a, groupby=a.ix[:,1])
And this is what I get:
However, what I would have expected to get was to have two boxplots each describing only the first column, grouped by their corresponding column in the second column (the column converted to Series), while the above plot shows each column separately which is not what I want.
A column in a Dataframe is already a Series, so your conversion is not necessary. Furthermore, if you only want to use the first column for both boxplots, you should only pass that to Seaborn.
So:
#example data for reproduciblity
df = pd.DataFrame(
[
[2, 1],
[4, 2],
[5, 1],
[10, 2],
[9, 2],
[3, 1]
], columns=['a', 'b'])
#Plotting by seaborn
sns.boxplot(df.a, groupby=df.b)
I changed your example a little bit, giving columns a label makes it a bit more clear in my opinion.
edit:
If you want to plot all columns separately you (i think) basically want all combinations of the values in your groupby column and any other column. So if you Dataframe looks like this:
a b grouper
0 2 5 1
1 4 9 2
2 5 3 1
3 10 6 2
4 9 7 2
5 3 11 1
And you want boxplots for columns a and b while grouped by the column grouper. You should flatten the columns and change the groupby column to contain values like a1, a2, b1 etc.
Here is a crude way which i think should work, given the Dataframe shown above:
dfpiv = df.pivot(index=df.index, columns='grouper')
cols_flat = [dfpiv.columns.levels[0][i] + str(dfpiv.columns.levels[1][j]) for i, j in zip(dfpiv.columns.labels[0], dfpiv.columns.labels[1])]
dfpiv.columns = cols_flat
dfpiv = dfpiv.stack(0)
sns.boxplot(dfpiv, groupby=dfpiv.index.get_level_values(1))
Perhaps there are more fancy ways of restructuring the Dataframe. Especially the flattening of the hierarchy after pivoting is hard to read, i dont like it.
This is a new answer for an old question because in seaborn and pandas are some changes through version updates. Because of this changes the answer of Rutger is not working anymore.
The most important changes are from seaborn==v0.5.x to seaborn==v0.6.0. I quote the log:
Changes to boxplot() and violinplot() will probably be the most disruptive. Both functions maintain backwards-compatibility in terms of the kind of data they can accept, but the syntax has changed to be more similar to other seaborn functions. These functions are now invoked with x and/or y parameters that are either vectors of data or names of variables in a long-form DataFrame passed to the new data parameter.
Let's now go through the examples:
# preamble
import pandas as pd # version 1.1.4
import seaborn as sns # version 0.11.0
sns.set_theme()
Example 1: Simple Boxplot
df = pd.DataFrame([[2, 1] ,[4, 2],[5, 1],
[10, 2],[9, 2],[3, 1]
], columns=['a', 'b'])
#Plotting by seaborn with x and y as parameter
sns.boxplot(x='b', y='a', data=df)
Example 2: Boxplot with grouper
df = pd.DataFrame([[2, 5, 1], [4, 9, 2],[5, 3, 1],
[10, 6, 2],[9, 7, 2],[3, 11, 1]
], columns=['a', 'b', 'grouper'])
# usinge pandas melt
df_long = pd.melt(df, "grouper", var_name='a', value_name='b')
# join two columns together
df_long['a'] = df_long['a'].astype(str) + df_long['grouper'].astype(str)
sns.boxplot(x='a', y='b', data=df_long)
Example 3: rearanging the DataFrame to pass is directly to seaborn
def df_rename_by_group(data:pd.DataFrame, col:str)->pd.DataFrame:
'''This function takes a DataFrame, groups by one column and returns
a new DataFrame where the old columnnames are extended by the group item.
'''
grouper = df.groupby(col)
max_length_of_group = max([len(values) for item, values in grouper.indices.items()])
_df = pd.DataFrame(index=range(max_length_of_group))
for i in grouper.groups.keys():
helper = grouper.get_group(i).drop(col, axis=1).add_suffix(str(i))
helper.reset_index(drop=True, inplace=True)
_df = _df.join(helper)
return _df
df = pd.DataFrame([[2, 5, 1], [4, 9, 2],[5, 3, 1],
[10, 6, 2],[9, 7, 2],[3, 11, 1]
], columns=['a', 'b', 'grouper'])
df_new = df_rename_by_group(data=df, col='grouper')
sns.boxplot(data=df_new)
I really hope this answer helps to avoid some confusion.
sns.boxplot() doesnot take groupby.
Probably you are gonna see
TypeError: boxplot() got an unexpected keyword argument 'groupby'.
The best idea to group data and use in boxplot passing the data as groupby dataframe value.
import seaborn as sns
grouDataFrame = nameDataFrame(['A'])['B'].agg(sum).reset_index()
sns.boxplot(y='B', x='A', data=grouDataFrame)
Here B column data contains numeric value and grouped is done on the basis of A. All the grouped value with their respective column are added and boxplot diagram is plotted. Hope this helps.