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g = sns.*plot 
ax = g 
for p in ax.patches:
    ax.text(p.get_x() + p.get_width()/2., p.get_height(), '{0:.2f}'.format(p.get_height()), 
        fontsize=12, color='black', ha='center', va='bottom')
django-admin startproject mysite
python startapp myapp
import pandas as pd
import matplotlib.pyplot as plt

from pandas_profiling import ProfileReport
profile = ProfileReport(gabijos, title='Gabijos g.', html={'style':{'full_width':True}})

df_query = df_query.assign(comments='NoComment')
qq= dff[~df.astype(str).apply(tuple, 1).isin(dff.astype(str).apply(tuple, 1))]
for p in ax.patches:
    values = '{:.0f}'.format(p.get_height())
    x = p.get_x() + p.get_width()/2
    y = p.get_height()
    ax.annotate(values, (x, y),ha='center', va ='bottom', fontsize = 10)
.apply(lambda x: x.replace(',',',').replace(',',',').split(',')
# importing libraries 
from sklearn.ensemble import VotingClassifier ,BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier
from sklearn.metrics import accuracy_score 
from numpy import mean,std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score,RepeatedStratifiedKFold,train_test_split
from sklearn.linear_model import LogisticRegression,RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from matplotlib import pyplot
from sklearn.datasets import load_wine,load_iris
from matplotlib.pyplot import figure
figure(num=2, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')
import xgboost as xgb
from sklearn.feature_selection import SelectKBest,f_regression
from sklearn.linear_model import LinearRegression,BayesianRidge,ElasticNet,Lasso,SGDRegressor,Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.preprocessing import LabelEncoder,OneHotEncoder,RobustScaler,StandardScaler
from sklearn.pipeline import make_pipeline,Pipeline
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA,KernelPCA
from sklearn.ensemble import ExtraTreesRegressor,GradientBoostingRegressor,RandomForestRegressor,VotingClassifier
from sklearn.model_selection import cross_val_score,KFold,GridSearchCV,RandomizedSearchCV,StratifiedKFold,train_test_split
from sklearn.base import BaseEstimator,clone,TransformerMixin,RegressorMixin
from sklearn.svm import LinearSVR,SVR
#import xgboost 
from xgboost import XGBRegressor
#Import Pandas
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import warnings
from scipy.stats import skew
from scipy.stats.stats import pearsonr
%matplotlib inline
seed = 1075
c3 = pd.Series(['China', 'US'])
# applying filter function 
df.filter(["Name", "College", "Salary"]) 

# importing pandas as pd 
import pandas as pd 
# Creating the dataframe  
df = pd.read_csv("nba.csv") 
# Using regular expression to extract all 
# columns which has letter 'a' or 'A' in its name. 
df.filter(regex ='[aA]') 
(df.groupby('name')['ext price']
 .agg(['mean', 'sum'])
df['column_name'] = pd.to_datetime(df['column_name'])
# new version
df.groupby(pd.Grouper(key='column_name', freq="M")).mean().plot()
def ffill_cols(df, cols_to_fill_name='Unn'):
    Forward fills column names. Propagate last valid column name forward to next invalid column. Works similarly to pandas
    :param df: pandas Dataframe; Dataframe
    :param cols_to_fill_name: str; The name of the columns you would like forward filled. Default is 'Unn' as
    the default name pandas gives unnamed columns is 'Unnamed'
    :returns: list; List of new column names
    cols = df.columns.to_list()
    for i, j in enumerate(cols):
        if j.startswith(cols_to_fill_name):
            cols[i] = cols[i-1]
    return cols

Thu May 13 2021 06:52:35 GMT+0000 (UTC)

#python #pandas

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#undefined #python #pandas

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#undefined #python #pandas

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#python #pandas

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#python #pandas

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#python #pandas

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#pandas #isin #filter

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#pandas #filter #column

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#pandas #filter

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#python #pandas #format #currency

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#python #pandas #formatting

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#pandas #duplicates #drop

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#python #pandas

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#python #pandas #grouper

Thu Aug 06 2020 08:57:00 GMT+0000 (UTC)

#python #pandas #data-cleaning

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