Python code data.csv import pandas as pd df = pd.read_csv('data.csv') print(df) Duration Pulse Maxpulse Calories 0 60 110 130 409.1 1 60 117 145 479.0 2 60 103 135 340.0 3 45 109 175 282.4 4 45 117 148 406.0 .. ... ... ... ... 164 60 105 140 290....
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns full_health_data = pd.read_csv("dataset.csv", header=0, sep=",") correlation_full_health = full_health_data.corr() axis_corr = sns.heatmap( correlation_full_health, vmin=-1, vmax=1, center=0, cma...
df = pd.read_csv('data.csv') x = df["Calories"].median()df.fillna({"Calories": x}, inplace=True) Try it Yourself » Median = the value in the middle, after you have sorted all values ascending.Example Calculate the MODE, and replace any empty values with it: import pandas ...
data.csv
import pandas as pdimport matplotlib.pyplot as pltfrom scipy import statsfull_health_data = pd.read_csv("data.csv", header=0, sep=",") x = full_health_data["Duration"]y = full_health_data ["Calorie_Burnage"]slope, intercept, r, p, std_err = stats.linregress(x, y)def myfunc(x...
import pandas as pdimport numpy as nphealth_data = pd.read_csv("data.csv", header=0, sep=",") x = health_data["Average_Pulse"]y = health_data["Calorie_Burnage"] slope_intercept = np.polyfit(x,y,1)print(slope_intercept) Try it Yourself » Example...
Python data.csv import pandas as pd df = pd.read_csv('data.csv') print(df.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 169 entries, 0 to 168 Data columns (total 4 columns): # Column Non-Null Count Dtype --- --- --- --- 0 Duration 169 non-n...
Python data.csv import pandas as pd df = pd.read_csv('data.csv') print(df.duplicated()) 0 False 1 False 2 False 3 False 4 False 5 False 6 False 7 False 8 False 9 False 10 False 11 False 12 True 13 False 14 False 15 False 16 False 17 False 18 False 19 ...
import pandas from sklearn import tree from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt df = pandas.read_csv("data.csv") d = {'UK': 0, 'USA': 1, 'N': 2} df['Nationality'] = df['Nationality'].map(d) d = {'YES': 1, 'NO': 0} df...
Python data.csv import pandas as pd df = pd.read_csv('data.csv') newdf = df.notnull() print(newdf.to_string()) #Note that we use the to_string() method to return the entire DataFrame. #Note: the rows 17, 27, 91, 118, 141 had Not-a-Number values in the...