def retweets_vs_likes(data):
tfav = pd.Series(data=data['Likes'].values, index=data['Date'])
tret = pd.Series(data=data['RTs'].values, index=data['Date'])
# Likes vs retweets visualization:
tfav.plot(figsize=(16, 4), label="Likes", legend=True)
tret.plot(figsize=(16, 4), label="Retweets", legend=True)
plt.show()
def nombre_tweets_candidates(data, candidate1, candidate2):
# Affiche le nombre de tweets contenant le nom du candidate1 et celui du candidate2
#création de deux nouvelles colonnes qui indiquent la présence des noms des candidats dans le tweet
data['Has_candidate1'] = (candidate1 in data['tweet_textual_content'])
data['Has_candidate2'] = (candidate2 in data['tweet_textual_content'])
#création des séries (nombre de likes pour candidat, date) pour candidate1 et candidate2
tcandidate1 = pd.Series(data[data['Has_candidate1'] == True]['Likes'],
index=data['Date'])
tcandidate2 = pd.Series(data[data['Has_candidate2'] == True]['Likes'],
index=data['Date'])
#visualisation
tcandidate1.plot(figsize=(16, 4), label="Candidate1", legend=True)
tcandidate2.plot(figsize=(16, 4), label="Candidate2", legend=True)
plt.show()
def generate_Z_msr_org(numOfBuses, numOfLines, bus_data_df, topo_mat,
file_name):
import pandas as pd
import numpy as np
import openpyxl
from openpyxl import load_workbook
# Creating Measurement Data to run state estimation
bus_data = bus_data_df[[
'Remote controlled bus number', 'Load MW', 'Generation MW'
]]
bus_data.columns = ['Bus number', 'Load', 'Generation']
# Correcting the load generation for a lossless DC system
correction_load = sum(bus_data['Load']) - sum(bus_data['Generation'])
print("correction_load: ", correction_load)
# Adding the correction load to the largest generator
bus_data['Generation'].loc[
bus_data['Generation'].idxmax()] += correction_load
# correction_check = sum(bus_data['Load']) - sum(bus_data['Generation'])
# print("correction_check: ", correction_check)
# Bus Power = Bus Gen - Bus Load
bus_data['Bus Power'] = bus_data['Generation'] - bus_data['Load']
print("bus_data:\n", bus_data.head())
# Padding 0 in the top of the data from reference
Z_data_bus_power = pd.DataFrame(
pd.concat([pd.Series([0]), bus_data['Bus Power']]))
# Topomat containing only the bus power rows along with reference bus
B_mat_bus_power = pd.concat(
[topo_mat.loc[0:0], topo_mat.loc[numOfLines * 2 + 1:]])
# Estimating the states fromt the bus power data
state_original = np.linalg.pinv(B_mat_bus_power) @ Z_data_bus_power
# Calculating the Z_msr_org using the Topology Matrix and states
Z_msr_org = topo_mat @ state_original
Z_msr_org.columns = ['Data']
# Saving the data
book = load_workbook(file_name)
writer = pd.ExcelWriter(file_name, engine='openpyxl')
writer.book = book
Z_msr_org.to_excel(writer, "Measurement Data", index=False)
bus_data.to_excel(writer, "Bus Data", index=False)
writer.save()
writer.close()
# saving complete !
print("Z_msr_org:\n", Z_msr_org.head())
return Z_msr_org, bus_data
df.head() # shows only the head list
df.tail() #
df.values
df["temperature"]
df["day"].head()
df["temperature"] > 20 # shows all the temperatures below 20 degrees from the temperatures.csv file
df["temperature"] < 0 # shows all the temperatures below 0 degrees from the file
df_cool = df[df["temperature"] < 0]
df_cool.head()
df_cool.to_csv("cool.csv") # saves a new csv file named cool.csv
#save stuff into a new file
df["temperature"].mean() # this shows the average
df["temperature"].max() # this shows the maximum temperature
df["temperature"].min() # this shows the minimum temperature
df["temperature"].value_counts(
) # it counts how many times a certain value occurs in a row/list
df["temperature"].value_counts().head()
snacks = pd.Series(["Mars", "Twix", "Oreo"])
snacks.value_counts()
df["temperature"].plot() # shows a table of content thing (grafiek)
import panda as pd ps=pd.Series([1,2,3,4,5]) ps1=pd.Series([5,6,7,8,9]) ps+ps1
MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],
labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
#set index names
df.index.names #check how many name you need
df.index.names = ['Group','Num']
#select the first index
df.xs('G1')
df.xs(['G1',1])
df.xs(1,level='Num')
#GROUP BY function
df.groupby('Company') #supportive function .mean(), std()
#Create a series in Pando - A Series is very similar to a NumPy array. A Series can have axis labels, meaning it can be indexed by a label, instead of just a number location.
#It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.
pd.Series(data=my_list) #set label "index=labels"
#Can do normal operation just like matrices
#Operations To Find Unique
df['ColName'].unique #return all unique number
df['ColName'].nunique #return the number of unique number
df['col2'].value_counts() #return the count of each unique number
#applying function
def times2(x):
return x*2
df['col1'].apply(times2)
df['col3'].apply(len) #length
#Get Column and index names:
df.columns '''return the column''' df.head() #return the top 5
>>> df.apply(np.cumsum)
A B C D
2013-01-01 0.070158 0.629035 0.199517 -0.157134
2013-01-02 -0.587144 -0.100147 1.749539 -0.137846
2013-01-03 0.380278 0.125308 2.642619 -0.686718
2013-01-04 1.600488 1.067800 2.316169 -0.986318
2013-01-05 1.730403 -0.289345 0.987189 -0.602161
2013-01-06 2.386028 -0.005460 2.269278 -1.798823
>>> df.apply(lambda x: x.max() - x.min())
A 1.877512
B 2.299638
C 2.879002
D 1.580820
dtype: float64
>>> #HISTOGRAMMING
>>> s=pd.Series(np.random.randint(0,7,size=10))
>>> s
0 4
1 4
2 2
3 4
4 1
5 6
6 2
7 2
8 1
9 1
dtype: int32
>>> s.value_counts()
4 3
2 3
