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Why are sns.lmplot and FacetGrid + plt.scatter creating different scatter points for the same data?

I am new to Python, pandas DataFrames and Seaborn. When I tried to understand Seaborn better, especially sns.lmplot, I came across the difference between two digits from the same data, which I thought should have looked the same, and I wonder why that is.

Data: My data is a pandas DataFrame that contains 454 rows and 19 columns. The data related to this question includes 4 columns and looks something like this:

Columns: Av_density; pred2; LOC; Year;

Variable Type: Continuous Variable; Continuous variable; Categorical variable 1 ... 4, categorical 2012 ... 2014

Missing data points missing.

My goal is to draw a 2x2 shape panel describing the relationship between Av_density and pred2 separately for each LOC (= location) with years marked in different colors. I call the seabed:

import seaborn as sns
sns.set(style="whitegrid")
np.random.seed(sum(map(ord, "linear_categorical")))

(Lateral point: for some reason, calling "linear_quantitative" does not work, that is, I get "File" stdin ", line 2 sns.lmplot (" Av_density "," pred2 ", Data, col =" LOC ", hue = "YEAR", col_wrap = 2);
 ^ SyntaxError : invalid syntax ")

Figure 1 method, FacetGrid + scatter:

sur=sns.FacetGrid(Data,col="LOC", col_wrap=2,hue="YEAR")
sur.map(plt.scatter, "Av_density", "pred2" );
plt.legend()

This gives a good spread of data for sure. You can see the image here: https://drive.google.com/file/d/0B7h2wsx9mUBScEdUbGRlRk5PV1E/view?usp=sharing

Figure 2 method, sns.lmplot:

sns.lmplot("Av_density", "pred2", Data, col="LOC", hue="YEAR", col_wrap=2);

, , LOC , , . , , lmplot , , , . : https://drive.google.com/file/d/0B7h2wsx9mUBSRkN5ZXhBeW9ob1E/view?usp=sharing

, , "" datapoint lmplot. , , .

, , -, , , lmplot, ?

, !

-TA -

Ps. Python 2.7.8 Spyder 2.3.4

EDIT: " " , :

sur.map(plt.plot,"Av_density", "pred2" );

, lmplot.

+4
1

, , y, . y , , , , .

fig1 = sns.lmplot("Av_density", "pred2", Data, col="LOC", hue="YEAR", col_wrap=2);
fig1.set(ylim=(-0.03, 0.05))
plt.show(fig1)
+2

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